Overview

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Dataset statistics

Number of variables59
Number of observations20
Missing cells590
Missing cells (%)50.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.4 KiB
Average record size in memory943.9 B

Variable types

Text3
Categorical26
Numeric30

Alerts

PAQ_A-Season has constant value "Summer"Constant
PAQ_A-PAQ_A_Total has constant value "1.04"Constant
BIA-BIA_Activity_Level_num is highly overall correlated with BIA-BIA_BMC and 16 other fieldsHigh correlation
BIA-BIA_BMC is highly overall correlated with BIA-BIA_Activity_Level_num and 26 other fieldsHigh correlation
BIA-BIA_BMI is highly overall correlated with BIA-BIA_BMC and 31 other fieldsHigh correlation
BIA-BIA_BMR is highly overall correlated with BIA-BIA_Activity_Level_num and 30 other fieldsHigh correlation
BIA-BIA_DEE is highly overall correlated with BIA-BIA_Activity_Level_num and 27 other fieldsHigh correlation
BIA-BIA_ECW is highly overall correlated with BIA-BIA_BMC and 25 other fieldsHigh correlation
BIA-BIA_FFM is highly overall correlated with BIA-BIA_Activity_Level_num and 30 other fieldsHigh correlation
BIA-BIA_FFMI is highly overall correlated with BIA-BIA_BMC and 31 other fieldsHigh correlation
BIA-BIA_FMI is highly overall correlated with BIA-BIA_BMC and 29 other fieldsHigh correlation
BIA-BIA_Fat is highly overall correlated with BIA-BIA_BMC and 28 other fieldsHigh correlation
BIA-BIA_Frame_num is highly overall correlated with BIA-BIA_BMC and 10 other fieldsHigh correlation
BIA-BIA_ICW is highly overall correlated with BIA-BIA_BMC and 26 other fieldsHigh correlation
BIA-BIA_LDM is highly overall correlated with BIA-BIA_BMC and 22 other fieldsHigh correlation
BIA-BIA_LST is highly overall correlated with BIA-BIA_Activity_Level_num and 30 other fieldsHigh correlation
BIA-BIA_SMM is highly overall correlated with BIA-BIA_BMC and 23 other fieldsHigh correlation
BIA-BIA_TBW is highly overall correlated with BIA-BIA_BMC and 25 other fieldsHigh correlation
BIA-Season is highly overall correlated with FGC-FGC_CU and 10 other fieldsHigh correlation
Basic_Demos-Age is highly overall correlated with BIA-BIA_BMC and 23 other fieldsHigh correlation
Basic_Demos-Enroll_Season is highly overall correlated with BIA-BIA_BMI and 10 other fieldsHigh correlation
Basic_Demos-Sex is highly overall correlated with BIA-BIA_ECW and 7 other fieldsHigh correlation
CGAS-CGAS_Score is highly overall correlated with BIA-BIA_Activity_Level_num and 11 other fieldsHigh correlation
CGAS-Season is highly overall correlated with BIA-BIA_Activity_Level_num and 20 other fieldsHigh correlation
FGC-FGC_CU is highly overall correlated with BIA-BIA_BMC and 25 other fieldsHigh correlation
FGC-FGC_CU_Zone is highly overall correlated with BIA-BIA_BMI and 9 other fieldsHigh correlation
FGC-FGC_GSD is highly overall correlated with BIA-BIA_Activity_Level_num and 50 other fieldsHigh correlation
FGC-FGC_GSD_Zone is highly overall correlated with BIA-BIA_Activity_Level_num and 29 other fieldsHigh correlation
FGC-FGC_GSND is highly overall correlated with BIA-BIA_Activity_Level_num and 50 other fieldsHigh correlation
FGC-FGC_GSND_Zone is highly overall correlated with BIA-BIA_Activity_Level_num and 26 other fieldsHigh correlation
FGC-FGC_PU is highly overall correlated with BIA-BIA_Activity_Level_num and 23 other fieldsHigh correlation
FGC-FGC_PU_Zone is highly overall correlated with BIA-BIA_Activity_Level_num and 17 other fieldsHigh correlation
FGC-FGC_SRL is highly overall correlated with Basic_Demos-Sex and 11 other fieldsHigh correlation
FGC-FGC_SRL_Zone is highly overall correlated with BIA-BIA_BMI and 11 other fieldsHigh correlation
FGC-FGC_SRR is highly overall correlated with BIA-BIA_Activity_Level_num and 11 other fieldsHigh correlation
FGC-FGC_SRR_Zone is highly overall correlated with BIA-BIA_BMI and 10 other fieldsHigh correlation
FGC-FGC_TL is highly overall correlated with BIA-BIA_BMC and 23 other fieldsHigh correlation
FGC-FGC_TL_Zone is highly overall correlated with FGC-FGC_GSD and 7 other fieldsHigh correlation
FGC-Season is highly overall correlated with BIA-BIA_BMR and 10 other fieldsHigh correlation
Fitness_Endurance-Max_Stage is highly overall correlated with Basic_Demos-Age and 31 other fieldsHigh correlation
Fitness_Endurance-Time_Mins is highly overall correlated with Basic_Demos-Age and 31 other fieldsHigh correlation
Fitness_Endurance-Time_Sec is highly overall correlated with Basic_Demos-Age and 31 other fieldsHigh correlation
PAQ_C-PAQ_C_Total is highly overall correlated with BIA-BIA_BMI and 22 other fieldsHigh correlation
PAQ_C-Season is highly overall correlated with BIA-BIA_BMI and 22 other fieldsHigh correlation
Physical-BMI is highly overall correlated with BIA-BIA_BMI and 12 other fieldsHigh correlation
Physical-Diastolic_BP is highly overall correlated with BIA-BIA_DEE and 9 other fieldsHigh correlation
Physical-HeartRate is highly overall correlated with CGAS-CGAS_Score and 11 other fieldsHigh correlation
Physical-Height is highly overall correlated with BIA-BIA_BMC and 23 other fieldsHigh correlation
Physical-Season is highly overall correlated with BIA-BIA_BMR and 11 other fieldsHigh correlation
Physical-Systolic_BP is highly overall correlated with CGAS-CGAS_Score and 6 other fieldsHigh correlation
Physical-Waist_Circumference is highly overall correlated with BIA-BIA_Activity_Level_num and 32 other fieldsHigh correlation
Physical-Weight is highly overall correlated with BIA-BIA_BMC and 28 other fieldsHigh correlation
PreInt_EduHx-Season is highly overall correlated with BIA-BIA_BMI and 10 other fieldsHigh correlation
PreInt_EduHx-computerinternet_hoursday is highly overall correlated with BIA-BIA_Activity_Level_num and 7 other fieldsHigh correlation
SDS-SDS_Total_Raw is highly overall correlated with BIA-BIA_Activity_Level_num and 12 other fieldsHigh correlation
SDS-SDS_Total_T is highly overall correlated with FGC-FGC_GSD and 10 other fieldsHigh correlation
SDS-Season is highly overall correlated with BIA-BIA_BMR and 11 other fieldsHigh correlation
CGAS-Season has 10 (50.0%) missing valuesMissing
CGAS-CGAS_Score has 12 (60.0%) missing valuesMissing
Physical-Season has 6 (30.0%) missing valuesMissing
Physical-BMI has 7 (35.0%) missing valuesMissing
Physical-Height has 7 (35.0%) missing valuesMissing
Physical-Weight has 7 (35.0%) missing valuesMissing
Physical-Waist_Circumference has 15 (75.0%) missing valuesMissing
Physical-Diastolic_BP has 9 (45.0%) missing valuesMissing
Physical-HeartRate has 8 (40.0%) missing valuesMissing
Physical-Systolic_BP has 9 (45.0%) missing valuesMissing
Fitness_Endurance-Season has 16 (80.0%) missing valuesMissing
Fitness_Endurance-Max_Stage has 17 (85.0%) missing valuesMissing
Fitness_Endurance-Time_Mins has 17 (85.0%) missing valuesMissing
Fitness_Endurance-Time_Sec has 17 (85.0%) missing valuesMissing
FGC-Season has 3 (15.0%) missing valuesMissing
FGC-FGC_CU has 7 (35.0%) missing valuesMissing
FGC-FGC_CU_Zone has 7 (35.0%) missing valuesMissing
FGC-FGC_GSND has 15 (75.0%) missing valuesMissing
FGC-FGC_GSND_Zone has 15 (75.0%) missing valuesMissing
FGC-FGC_GSD has 15 (75.0%) missing valuesMissing
FGC-FGC_GSD_Zone has 15 (75.0%) missing valuesMissing
FGC-FGC_PU has 7 (35.0%) missing valuesMissing
FGC-FGC_PU_Zone has 7 (35.0%) missing valuesMissing
FGC-FGC_SRL has 7 (35.0%) missing valuesMissing
FGC-FGC_SRL_Zone has 7 (35.0%) missing valuesMissing
FGC-FGC_SRR has 7 (35.0%) missing valuesMissing
FGC-FGC_SRR_Zone has 7 (35.0%) missing valuesMissing
FGC-FGC_TL has 7 (35.0%) missing valuesMissing
FGC-FGC_TL_Zone has 7 (35.0%) missing valuesMissing
BIA-Season has 12 (60.0%) missing valuesMissing
BIA-BIA_Activity_Level_num has 12 (60.0%) missing valuesMissing
BIA-BIA_BMC has 12 (60.0%) missing valuesMissing
BIA-BIA_BMI has 12 (60.0%) missing valuesMissing
BIA-BIA_BMR has 12 (60.0%) missing valuesMissing
BIA-BIA_DEE has 12 (60.0%) missing valuesMissing
BIA-BIA_ECW has 12 (60.0%) missing valuesMissing
BIA-BIA_FFM has 12 (60.0%) missing valuesMissing
BIA-BIA_FFMI has 12 (60.0%) missing valuesMissing
BIA-BIA_FMI has 12 (60.0%) missing valuesMissing
BIA-BIA_Fat has 12 (60.0%) missing valuesMissing
BIA-BIA_Frame_num has 12 (60.0%) missing valuesMissing
BIA-BIA_ICW has 12 (60.0%) missing valuesMissing
BIA-BIA_LDM has 12 (60.0%) missing valuesMissing
BIA-BIA_LST has 12 (60.0%) missing valuesMissing
BIA-BIA_SMM has 12 (60.0%) missing valuesMissing
BIA-BIA_TBW has 12 (60.0%) missing valuesMissing
PAQ_A-Season has 19 (95.0%) missing valuesMissing
PAQ_A-PAQ_A_Total has 19 (95.0%) missing valuesMissing
PAQ_C-Season has 11 (55.0%) missing valuesMissing
PAQ_C-PAQ_C_Total has 11 (55.0%) missing valuesMissing
SDS-Season has 10 (50.0%) missing valuesMissing
SDS-SDS_Total_Raw has 10 (50.0%) missing valuesMissing
SDS-SDS_Total_T has 10 (50.0%) missing valuesMissing
PreInt_EduHx-Season has 2 (10.0%) missing valuesMissing
PreInt_EduHx-computerinternet_hoursday has 4 (20.0%) missing valuesMissing
Fitness_Endurance-Max_Stage is uniformly distributedUniform
Fitness_Endurance-Time_Mins is uniformly distributedUniform
Fitness_Endurance-Time_Sec is uniformly distributedUniform
FGC-FGC_GSND is uniformly distributedUniform
FGC-FGC_GSD is uniformly distributedUniform
id has unique valuesUnique
FGC-FGC_CU has 3 (15.0%) zerosZeros
FGC-FGC_PU has 6 (30.0%) zerosZeros
FGC-FGC_SRL has 2 (10.0%) zerosZeros
FGC-FGC_SRR has 2 (10.0%) zerosZeros

Reproduction

Analysis started2024-10-11 10:22:02.157913
Analysis finished2024-10-11 10:26:10.916911
Duration4 minutes and 8.76 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

id
Text

UNIQUE 

Distinct20
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-10-11T10:26:11.202684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters160
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row00008ff9
2nd row000fd460
3rd row00105258
4th row00115b9f
5th row0016bb22
ValueCountFrequency (%)
00008ff9 1
 
5.0%
000fd460 1
 
5.0%
00105258 1
 
5.0%
00115b9f 1
 
5.0%
0016bb22 1
 
5.0%
001f3379 1
 
5.0%
0038ba98 1
 
5.0%
0068a485 1
 
5.0%
0069fbed 1
 
5.0%
0083e397 1
 
5.0%
Other values (10) 10
50.0%
2024-10-11T10:26:11.841463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 46
28.7%
9 11
 
6.9%
d 11
 
6.9%
5 11
 
6.9%
6 10
 
6.2%
8 9
 
5.6%
3 9
 
5.6%
b 9
 
5.6%
1 8
 
5.0%
f 7
 
4.4%
Other values (6) 29
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 46
28.7%
9 11
 
6.9%
d 11
 
6.9%
5 11
 
6.9%
6 10
 
6.2%
8 9
 
5.6%
3 9
 
5.6%
b 9
 
5.6%
1 8
 
5.0%
f 7
 
4.4%
Other values (6) 29
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 46
28.7%
9 11
 
6.9%
d 11
 
6.9%
5 11
 
6.9%
6 10
 
6.2%
8 9
 
5.6%
3 9
 
5.6%
b 9
 
5.6%
1 8
 
5.0%
f 7
 
4.4%
Other values (6) 29
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 46
28.7%
9 11
 
6.9%
d 11
 
6.9%
5 11
 
6.9%
6 10
 
6.2%
8 9
 
5.6%
3 9
 
5.6%
b 9
 
5.6%
1 8
 
5.0%
f 7
 
4.4%
Other values (6) 29
18.1%

Basic_Demos-Enroll_Season
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Fall
Spring
Summer
Winter

Length

Max length6
Median length6
Mean length5.4
Min length4

Characters and Unicode

Total characters108
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFall
2nd rowSummer
3rd rowSummer
4th rowWinter
5th rowSpring

Common Values

ValueCountFrequency (%)
Fall 6
30.0%
Spring 6
30.0%
Summer 4
20.0%
Winter 4
20.0%

Length

2024-10-11T10:26:12.154850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:12.453729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fall 6
30.0%
spring 6
30.0%
summer 4
20.0%
winter 4
20.0%

Most occurring characters

ValueCountFrequency (%)
r 14
13.0%
l 12
11.1%
S 10
9.3%
i 10
9.3%
n 10
9.3%
m 8
7.4%
e 8
7.4%
F 6
 
5.6%
a 6
 
5.6%
p 6
 
5.6%
Other values (4) 18
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 14
13.0%
l 12
11.1%
S 10
9.3%
i 10
9.3%
n 10
9.3%
m 8
7.4%
e 8
7.4%
F 6
 
5.6%
a 6
 
5.6%
p 6
 
5.6%
Other values (4) 18
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 14
13.0%
l 12
11.1%
S 10
9.3%
i 10
9.3%
n 10
9.3%
m 8
7.4%
e 8
7.4%
F 6
 
5.6%
a 6
 
5.6%
p 6
 
5.6%
Other values (4) 18
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 14
13.0%
l 12
11.1%
S 10
9.3%
i 10
9.3%
n 10
9.3%
m 8
7.4%
e 8
7.4%
F 6
 
5.6%
a 6
 
5.6%
p 6
 
5.6%
Other values (4) 18
16.7%

Basic_Demos-Age
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.75
Minimum5
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:12.690918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q19
median10
Q312.25
95-th percentile18.05
Maximum19
Range14
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation3.7257991
Coefficient of variation (CV)0.34658596
Kurtosis0.4687184
Mean10.75
Median Absolute Deviation (MAD)2
Skewness0.55757412
Sum215
Variance13.881579
MonotonicityNot monotonic
2024-10-11T10:26:12.900322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
10 5
25.0%
5 2
 
10.0%
9 2
 
10.0%
13 2
 
10.0%
11 2
 
10.0%
12 2
 
10.0%
18 1
 
5.0%
15 1
 
5.0%
19 1
 
5.0%
7 1
 
5.0%
ValueCountFrequency (%)
5 2
 
10.0%
6 1
 
5.0%
7 1
 
5.0%
9 2
 
10.0%
10 5
25.0%
11 2
 
10.0%
12 2
 
10.0%
13 2
 
10.0%
15 1
 
5.0%
18 1
 
5.0%
ValueCountFrequency (%)
19 1
 
5.0%
18 1
 
5.0%
15 1
 
5.0%
13 2
 
10.0%
12 2
 
10.0%
11 2
 
10.0%
10 5
25.0%
9 2
 
10.0%
7 1
 
5.0%
6 1
 
5.0%

Basic_Demos-Sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
0
12 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 12
60.0%
1 8
40.0%

Length

2024-10-11T10:26:13.163810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:13.492942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12
60.0%
1 8
40.0%

Most occurring characters

ValueCountFrequency (%)
0 12
60.0%
1 8
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 12
60.0%
1 8
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 12
60.0%
1 8
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 12
60.0%
1 8
40.0%

CGAS-Season
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)40.0%
Missing10
Missing (%)50.0%
Memory size1.3 KiB
Summer
Winter
Fall
Spring

Length

Max length6
Median length6
Mean length5.6
Min length4

Characters and Unicode

Total characters56
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st rowWinter
2nd rowFall
3rd rowFall
4th rowSummer
5th rowWinter

Common Values

ValueCountFrequency (%)
Summer 5
25.0%
Winter 2
 
10.0%
Fall 2
 
10.0%
Spring 1
 
5.0%
(Missing) 10
50.0%

Length

2024-10-11T10:26:13.946894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:14.244604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
summer 5
50.0%
winter 2
 
20.0%
fall 2
 
20.0%
spring 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
m 10
17.9%
r 8
14.3%
e 7
12.5%
S 6
10.7%
u 5
8.9%
l 4
 
7.1%
i 3
 
5.4%
n 3
 
5.4%
W 2
 
3.6%
t 2
 
3.6%
Other values (4) 6
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 10
17.9%
r 8
14.3%
e 7
12.5%
S 6
10.7%
u 5
8.9%
l 4
 
7.1%
i 3
 
5.4%
n 3
 
5.4%
W 2
 
3.6%
t 2
 
3.6%
Other values (4) 6
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 10
17.9%
r 8
14.3%
e 7
12.5%
S 6
10.7%
u 5
8.9%
l 4
 
7.1%
i 3
 
5.4%
n 3
 
5.4%
W 2
 
3.6%
t 2
 
3.6%
Other values (4) 6
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 10
17.9%
r 8
14.3%
e 7
12.5%
S 6
10.7%
u 5
8.9%
l 4
 
7.1%
i 3
 
5.4%
n 3
 
5.4%
W 2
 
3.6%
t 2
 
3.6%
Other values (4) 6
10.7%

CGAS-CGAS_Score
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)75.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean62.5
Minimum50
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:14.616126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile50.35
Q151
median63
Q371
95-th percentile76.85
Maximum80
Range30
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.275764
Coefficient of variation (CV)0.18041223
Kurtosis-1.4256281
Mean62.5
Median Absolute Deviation (MAD)10
Skewness0.21523741
Sum500
Variance127.14286
MonotonicityNot monotonic
2024-10-11T10:26:15.020951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
51 2
 
10.0%
71 2
 
10.0%
50 1
 
5.0%
66 1
 
5.0%
80 1
 
5.0%
60 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
50 1
5.0%
51 2
10.0%
60 1
5.0%
66 1
5.0%
71 2
10.0%
80 1
5.0%
ValueCountFrequency (%)
80 1
5.0%
71 2
10.0%
66 1
5.0%
60 1
5.0%
51 2
10.0%
50 1
5.0%

Physical-Season
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)28.6%
Missing6
Missing (%)30.0%
Memory size1.3 KiB
Fall
Spring
Summer
Winter

Length

Max length6
Median length6
Mean length5.1428571
Min length4

Characters and Unicode

Total characters72
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFall
2nd rowFall
3rd rowFall
4th rowSummer
5th rowSummer

Common Values

ValueCountFrequency (%)
Fall 6
30.0%
Spring 4
20.0%
Summer 2
 
10.0%
Winter 2
 
10.0%
(Missing) 6
30.0%

Length

2024-10-11T10:26:15.394242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:15.866813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fall 6
42.9%
spring 4
28.6%
summer 2
 
14.3%
winter 2
 
14.3%

Most occurring characters

ValueCountFrequency (%)
l 12
16.7%
r 8
11.1%
F 6
8.3%
a 6
8.3%
S 6
8.3%
i 6
8.3%
n 6
8.3%
p 4
 
5.6%
g 4
 
5.6%
m 4
 
5.6%
Other values (4) 10
13.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 12
16.7%
r 8
11.1%
F 6
8.3%
a 6
8.3%
S 6
8.3%
i 6
8.3%
n 6
8.3%
p 4
 
5.6%
g 4
 
5.6%
m 4
 
5.6%
Other values (4) 10
13.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 12
16.7%
r 8
11.1%
F 6
8.3%
a 6
8.3%
S 6
8.3%
i 6
8.3%
n 6
8.3%
p 4
 
5.6%
g 4
 
5.6%
m 4
 
5.6%
Other values (4) 10
13.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 12
16.7%
r 8
11.1%
F 6
8.3%
a 6
8.3%
S 6
8.3%
i 6
8.3%
n 6
8.3%
p 4
 
5.6%
g 4
 
5.6%
m 4
 
5.6%
Other values (4) 10
13.9%

Physical-BMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing7
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean19.835939
Minimum14.03559
Maximum30.094649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:16.219744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.03559
5-th percentile14.940703
Q116.861286
median18.292347
Q321.079065
95-th percentile29.627325
Maximum30.094649
Range16.059059
Interquartile range (IQR)4.2177788

Descriptive statistics

Standard deviation4.9276245
Coefficient of variation (CV)0.24841902
Kurtosis0.97603309
Mean19.835939
Median Absolute Deviation (MAD)1.6436509
Skewness1.2914504
Sum257.86721
Variance24.281483
MonotonicityNot monotonic
2024-10-11T10:26:16.608406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
16.87731569 1
 
5.0%
14.03559028 1
 
5.0%
16.64869606 1
 
5.0%
18.29234694 1
 
5.0%
22.27995198 1
 
5.0%
19.66076033 1
 
5.0%
16.86128647 1
 
5.0%
21.07906523 1
 
5.0%
15.54411111 1
 
5.0%
29.31577503 1
 
5.0%
Other values (3) 3
15.0%
(Missing) 7
35.0%
ValueCountFrequency (%)
14.03559028 1
5.0%
15.54411111 1
5.0%
16.64869606 1
5.0%
16.86128647 1
5.0%
16.87731569 1
5.0%
17.28450413 1
5.0%
18.29234694 1
5.0%
19.66076033 1
5.0%
19.89315702 1
5.0%
21.07906523 1
5.0%
ValueCountFrequency (%)
30.09464889 1
5.0%
29.31577503 1
5.0%
22.27995198 1
5.0%
21.07906523 1
5.0%
19.89315702 1
5.0%
19.66076033 1
5.0%
18.29234694 1
5.0%
17.28450413 1
5.0%
16.87731569 1
5.0%
16.86128647 1
5.0%

Physical-Height
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)92.3%
Missing7
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean52.961538
Minimum37.5
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:17.014536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum37.5
5-th percentile41.4
Q148
median55
Q357.75
95-th percentile59.7
Maximum60
Range22.5
Interquartile range (IQR)9.75

Descriptive statistics

Standard deviation6.9423565
Coefficient of variation (CV)0.13108298
Kurtosis0.41779506
Mean52.961538
Median Absolute Deviation (MAD)4.25
Skewness-1.1179984
Sum688.5
Variance48.196314
MonotonicityNot monotonic
2024-10-11T10:26:17.460399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
55 2
 
10.0%
46 1
 
5.0%
48 1
 
5.0%
56.5 1
 
5.0%
56 1
 
5.0%
59.5 1
 
5.0%
59.25 1
 
5.0%
57.75 1
 
5.0%
60 1
 
5.0%
54 1
 
5.0%
Other values (2) 2
 
10.0%
(Missing) 7
35.0%
ValueCountFrequency (%)
37.5 1
5.0%
44 1
5.0%
46 1
5.0%
48 1
5.0%
54 1
5.0%
55 2
10.0%
56 1
5.0%
56.5 1
5.0%
57.75 1
5.0%
59.25 1
5.0%
ValueCountFrequency (%)
60 1
5.0%
59.5 1
5.0%
59.25 1
5.0%
57.75 1
5.0%
56.5 1
5.0%
56 1
5.0%
55 2
10.0%
54 1
5.0%
48 1
5.0%
46 1
5.0%

Physical-Weight
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing7
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean79.2
Minimum46
Maximum121.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:17.993910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile46.96
Q160.2
median81.6
Q385.6
95-th percentile115.96
Maximum121.6
Range75.6
Interquartile range (IQR)25.4

Descriptive statistics

Standard deviation23.632181
Coefficient of variation (CV)0.29838613
Kurtosis-0.56703056
Mean79.2
Median Absolute Deviation (MAD)18.4
Skewness0.17728785
Sum1029.6
Variance558.48
MonotonicityNot monotonic
2024-10-11T10:26:18.350168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
50.8 1
 
5.0%
46 1
 
5.0%
75.6 1
 
5.0%
81.6 1
 
5.0%
112.2 1
 
5.0%
84.6 1
 
5.0%
84.2 1
 
5.0%
100 1
 
5.0%
79.6 1
 
5.0%
121.6 1
 
5.0%
Other values (3) 3
15.0%
(Missing) 7
35.0%
ValueCountFrequency (%)
46 1
5.0%
47.6 1
5.0%
50.8 1
5.0%
60.2 1
5.0%
75.6 1
5.0%
79.6 1
5.0%
81.6 1
5.0%
84.2 1
5.0%
84.6 1
5.0%
85.6 1
5.0%
ValueCountFrequency (%)
121.6 1
5.0%
112.2 1
5.0%
100 1
5.0%
85.6 1
5.0%
84.6 1
5.0%
84.2 1
5.0%
81.6 1
5.0%
79.6 1
5.0%
75.6 1
5.0%
60.2 1
5.0%

Physical-Waist_Circumference
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)80.0%
Missing15
Missing (%)75.0%
Memory size1.2 KiB
24.0
22.0
27.0
30.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters20
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)60.0%

Sample

1st row22.0
2nd row27.0
3rd row24.0
4th row30.0
5th row24.0

Common Values

ValueCountFrequency (%)
24.0 2
 
10.0%
22.0 1
 
5.0%
27.0 1
 
5.0%
30.0 1
 
5.0%
(Missing) 15
75.0%

Length

2024-10-11T10:26:18.603779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:18.844856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
24.0 2
40.0%
22.0 1
20.0%
27.0 1
20.0%
30.0 1
20.0%

Most occurring characters

ValueCountFrequency (%)
0 6
30.0%
2 5
25.0%
. 5
25.0%
4 2
 
10.0%
7 1
 
5.0%
3 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6
30.0%
2 5
25.0%
. 5
25.0%
4 2
 
10.0%
7 1
 
5.0%
3 1
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6
30.0%
2 5
25.0%
. 5
25.0%
4 2
 
10.0%
7 1
 
5.0%
3 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6
30.0%
2 5
25.0%
. 5
25.0%
4 2
 
10.0%
7 1
 
5.0%
3 1
 
5.0%

Physical-Diastolic_BP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)81.8%
Missing9
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean70.545455
Minimum57
Maximum123
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:19.076899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile58.5
Q160.5
median63
Q373
95-th percentile101.5
Maximum123
Range66
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation18.806189
Coefficient of variation (CV)0.26658257
Kurtosis7.0577996
Mean70.545455
Median Absolute Deviation (MAD)3
Skewness2.5375388
Sum776
Variance353.67273
MonotonicityNot monotonic
2024-10-11T10:26:19.282885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
60 2
 
10.0%
61 2
 
10.0%
75 1
 
5.0%
65 1
 
5.0%
123 1
 
5.0%
71 1
 
5.0%
63 1
 
5.0%
57 1
 
5.0%
80 1
 
5.0%
(Missing) 9
45.0%
ValueCountFrequency (%)
57 1
5.0%
60 2
10.0%
61 2
10.0%
63 1
5.0%
65 1
5.0%
71 1
5.0%
75 1
5.0%
80 1
5.0%
123 1
5.0%
ValueCountFrequency (%)
123 1
5.0%
80 1
5.0%
75 1
5.0%
71 1
5.0%
65 1
5.0%
63 1
5.0%
61 2
10.0%
60 2
10.0%
57 1
5.0%

Physical-HeartRate
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing8
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean81.666667
Minimum70
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:19.504613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile70.55
Q174.5
median80
Q390.25
95-th percentile95.35
Maximum97
Range27
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation9.3160012
Coefficient of variation (CV)0.11407348
Kurtosis-1.2927622
Mean81.666667
Median Absolute Deviation (MAD)8
Skewness0.39467745
Sum980
Variance86.787879
MonotonicityNot monotonic
2024-10-11T10:26:19.738060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
70 1
 
5.0%
94 1
 
5.0%
97 1
 
5.0%
73 1
 
5.0%
83 1
 
5.0%
90 1
 
5.0%
79 1
 
5.0%
71 1
 
5.0%
75 1
 
5.0%
76 1
 
5.0%
Other values (2) 2
 
10.0%
(Missing) 8
40.0%
ValueCountFrequency (%)
70 1
5.0%
71 1
5.0%
73 1
5.0%
75 1
5.0%
76 1
5.0%
79 1
5.0%
81 1
5.0%
83 1
5.0%
90 1
5.0%
91 1
5.0%
ValueCountFrequency (%)
97 1
5.0%
94 1
5.0%
91 1
5.0%
90 1
5.0%
83 1
5.0%
81 1
5.0%
79 1
5.0%
76 1
5.0%
75 1
5.0%
73 1
5.0%

Physical-Systolic_BP
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)90.9%
Missing9
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean117.54545
Minimum95
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:19.966819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile97
Q1102.5
median116
Q3119.5
95-th percentile156.5
Maximum163
Range68
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.262002
Coefficient of variation (CV)0.18088323
Kurtosis1.0935729
Mean117.54545
Median Absolute Deviation (MAD)13
Skewness1.3082682
Sum1293
Variance452.07273
MonotonicityNot monotonic
2024-10-11T10:26:20.180268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
117 2
 
10.0%
122 1
 
5.0%
102 1
 
5.0%
163 1
 
5.0%
116 1
 
5.0%
150 1
 
5.0%
103 1
 
5.0%
99 1
 
5.0%
109 1
 
5.0%
95 1
 
5.0%
(Missing) 9
45.0%
ValueCountFrequency (%)
95 1
5.0%
99 1
5.0%
102 1
5.0%
103 1
5.0%
109 1
5.0%
116 1
5.0%
117 2
10.0%
122 1
5.0%
150 1
5.0%
163 1
5.0%
ValueCountFrequency (%)
163 1
5.0%
150 1
5.0%
122 1
5.0%
117 2
10.0%
116 1
5.0%
109 1
5.0%
103 1
5.0%
102 1
5.0%
99 1
5.0%
95 1
5.0%
Distinct3
Distinct (%)75.0%
Missing16
Missing (%)80.0%
Memory size890.0 B
2024-10-11T10:26:20.411718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.5
Min length4

Characters and Unicode

Total characters22
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st rowFall
2nd rowSummer
3rd rowSpring
4th rowSpring
ValueCountFrequency (%)
spring 2
50.0%
fall 1
25.0%
summer 1
25.0%
2024-10-11T10:26:20.974950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3
13.6%
r 3
13.6%
p 2
9.1%
i 2
9.1%
n 2
9.1%
g 2
9.1%
l 2
9.1%
m 2
9.1%
F 1
 
4.5%
a 1
 
4.5%
Other values (2) 2
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 3
13.6%
r 3
13.6%
p 2
9.1%
i 2
9.1%
n 2
9.1%
g 2
9.1%
l 2
9.1%
m 2
9.1%
F 1
 
4.5%
a 1
 
4.5%
Other values (2) 2
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 3
13.6%
r 3
13.6%
p 2
9.1%
i 2
9.1%
n 2
9.1%
g 2
9.1%
l 2
9.1%
m 2
9.1%
F 1
 
4.5%
a 1
 
4.5%
Other values (2) 2
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 3
13.6%
r 3
13.6%
p 2
9.1%
i 2
9.1%
n 2
9.1%
g 2
9.1%
l 2
9.1%
m 2
9.1%
F 1
 
4.5%
a 1
 
4.5%
Other values (2) 2
9.1%

Fitness_Endurance-Max_Stage
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing17
Missing (%)85.0%
Memory size1.2 KiB
5.0
6.0
4.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row5.0
2nd row6.0
3rd row4.0

Common Values

ValueCountFrequency (%)
5.0 1
 
5.0%
6.0 1
 
5.0%
4.0 1
 
5.0%
(Missing) 17
85.0%

Length

2024-10-11T10:26:21.253741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:21.499350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
5.0 1
33.3%
6.0 1
33.3%
4.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
5 1
 
11.1%
6 1
 
11.1%
4 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
5 1
 
11.1%
6 1
 
11.1%
4 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
5 1
 
11.1%
6 1
 
11.1%
4 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
5 1
 
11.1%
6 1
 
11.1%
4 1
 
11.1%

Fitness_Endurance-Time_Mins
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing17
Missing (%)85.0%
Memory size1.2 KiB
7.0
9.0
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row7.0
2nd row9.0
3rd row5.0

Common Values

ValueCountFrequency (%)
7.0 1
 
5.0%
9.0 1
 
5.0%
5.0 1
 
5.0%
(Missing) 17
85.0%

Length

2024-10-11T10:26:21.716061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:21.959723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
7.0 1
33.3%
9.0 1
33.3%
5.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
7 1
 
11.1%
9 1
 
11.1%
5 1
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
7 1
 
11.1%
9 1
 
11.1%
5 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
7 1
 
11.1%
9 1
 
11.1%
5 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 3
33.3%
0 3
33.3%
7 1
 
11.1%
9 1
 
11.1%
5 1
 
11.1%

Fitness_Endurance-Time_Sec
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct3
Distinct (%)100.0%
Missing17
Missing (%)85.0%
Memory size1.2 KiB
33.0
37.0
32.0

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters12
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row33.0
2nd row37.0
3rd row32.0

Common Values

ValueCountFrequency (%)
33.0 1
 
5.0%
37.0 1
 
5.0%
32.0 1
 
5.0%
(Missing) 17
85.0%

Length

2024-10-11T10:26:22.199309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:22.439283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
33.0 1
33.3%
37.0 1
33.3%
32.0 1
33.3%

Most occurring characters

ValueCountFrequency (%)
3 4
33.3%
. 3
25.0%
0 3
25.0%
7 1
 
8.3%
2 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 4
33.3%
. 3
25.0%
0 3
25.0%
7 1
 
8.3%
2 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 4
33.3%
. 3
25.0%
0 3
25.0%
7 1
 
8.3%
2 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 4
33.3%
. 3
25.0%
0 3
25.0%
7 1
 
8.3%
2 1
 
8.3%

FGC-Season
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)23.5%
Missing3
Missing (%)15.0%
Memory size1.3 KiB
Fall
Spring
Winter
Summer

Length

Max length6
Median length6
Mean length5.2941176
Min length4

Characters and Unicode

Total characters90
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFall
2nd rowFall
3rd rowFall
4th rowSummer
5th rowSummer

Common Values

ValueCountFrequency (%)
Fall 6
30.0%
Spring 6
30.0%
Winter 3
15.0%
Summer 2
 
10.0%
(Missing) 3
15.0%

Length

2024-10-11T10:26:22.693926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:22.996969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fall 6
35.3%
spring 6
35.3%
winter 3
17.6%
summer 2
 
11.8%

Most occurring characters

ValueCountFrequency (%)
l 12
13.3%
r 11
12.2%
i 9
10.0%
n 9
10.0%
S 8
8.9%
F 6
6.7%
a 6
6.7%
p 6
6.7%
g 6
6.7%
e 5
5.6%
Other values (4) 12
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 12
13.3%
r 11
12.2%
i 9
10.0%
n 9
10.0%
S 8
8.9%
F 6
6.7%
a 6
6.7%
p 6
6.7%
g 6
6.7%
e 5
5.6%
Other values (4) 12
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 12
13.3%
r 11
12.2%
i 9
10.0%
n 9
10.0%
S 8
8.9%
F 6
6.7%
a 6
6.7%
p 6
6.7%
g 6
6.7%
e 5
5.6%
Other values (4) 12
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 12
13.3%
r 11
12.2%
i 9
10.0%
n 9
10.0%
S 8
8.9%
F 6
6.7%
a 6
6.7%
p 6
6.7%
g 6
6.7%
e 5
5.6%
Other values (4) 12
13.3%

FGC-FGC_CU
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)76.9%
Missing7
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean8.6923077
Minimum0
Maximum24
Zeros3
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:23.246796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q312
95-th percentile21.6
Maximum24
Range24
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.8992048
Coefficient of variation (CV)0.90875807
Kurtosis-0.47971486
Mean8.6923077
Median Absolute Deviation (MAD)6
Skewness0.72602317
Sum113
Variance62.397436
MonotonicityNot monotonic
2024-10-11T10:26:23.469062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 3
15.0%
6 2
 
10.0%
3 1
 
5.0%
20 1
 
5.0%
18 1
 
5.0%
12 1
 
5.0%
9 1
 
5.0%
24 1
 
5.0%
10 1
 
5.0%
5 1
 
5.0%
(Missing) 7
35.0%
ValueCountFrequency (%)
0 3
15.0%
3 1
 
5.0%
5 1
 
5.0%
6 2
10.0%
9 1
 
5.0%
10 1
 
5.0%
12 1
 
5.0%
18 1
 
5.0%
20 1
 
5.0%
24 1
 
5.0%
ValueCountFrequency (%)
24 1
 
5.0%
20 1
 
5.0%
18 1
 
5.0%
12 1
 
5.0%
10 1
 
5.0%
9 1
 
5.0%
6 2
10.0%
5 1
 
5.0%
3 1
 
5.0%
0 3
15.0%

FGC-FGC_CU_Zone
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)15.4%
Missing7
Missing (%)35.0%
Memory size1.3 KiB
0.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 7
35.0%
1.0 6
30.0%
(Missing) 7
35.0%

Length

2024-10-11T10:26:23.711777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:23.954402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 7
53.8%
1.0 6
46.2%

Most occurring characters

ValueCountFrequency (%)
0 20
51.3%
. 13
33.3%
1 6
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 20
51.3%
. 13
33.3%
1 6
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 20
51.3%
. 13
33.3%
1 6
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 20
51.3%
. 13
33.3%
1 6
 
15.4%

FGC-FGC_GSND
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct5
Distinct (%)100.0%
Missing15
Missing (%)75.0%
Memory size1.2 KiB
10.2
16.5
12.6
19.2
22.3

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters20
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row10.2
2nd row16.5
3rd row12.6
4th row19.2
5th row22.3

Common Values

ValueCountFrequency (%)
10.2 1
 
5.0%
16.5 1
 
5.0%
12.6 1
 
5.0%
19.2 1
 
5.0%
22.3 1
 
5.0%
(Missing) 15
75.0%

Length

2024-10-11T10:26:24.164451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:24.412569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
10.2 1
20.0%
16.5 1
20.0%
12.6 1
20.0%
19.2 1
20.0%
22.3 1
20.0%

Most occurring characters

ValueCountFrequency (%)
. 5
25.0%
2 5
25.0%
1 4
20.0%
6 2
 
10.0%
0 1
 
5.0%
5 1
 
5.0%
9 1
 
5.0%
3 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 5
25.0%
2 5
25.0%
1 4
20.0%
6 2
 
10.0%
0 1
 
5.0%
5 1
 
5.0%
9 1
 
5.0%
3 1
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 5
25.0%
2 5
25.0%
1 4
20.0%
6 2
 
10.0%
0 1
 
5.0%
5 1
 
5.0%
9 1
 
5.0%
3 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 5
25.0%
2 5
25.0%
1 4
20.0%
6 2
 
10.0%
0 1
 
5.0%
5 1
 
5.0%
9 1
 
5.0%
3 1
 
5.0%

FGC-FGC_GSND_Zone
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)40.0%
Missing15
Missing (%)75.0%
Memory size1.2 KiB
2.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 3
 
15.0%
1.0 2
 
10.0%
(Missing) 15
75.0%

Length

2024-10-11T10:26:24.645063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:24.874124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3
60.0%
1.0 2
40.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

FGC-FGC_GSD
Categorical

HIGH CORRELATION  MISSING  UNIFORM 

Distinct5
Distinct (%)100.0%
Missing15
Missing (%)75.0%
Memory size1.2 KiB
14.7
17.9
11.1
18.4
21.6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters20
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)100.0%

Sample

1st row14.7
2nd row17.9
3rd row11.1
4th row18.4
5th row21.6

Common Values

ValueCountFrequency (%)
14.7 1
 
5.0%
17.9 1
 
5.0%
11.1 1
 
5.0%
18.4 1
 
5.0%
21.6 1
 
5.0%
(Missing) 15
75.0%

Length

2024-10-11T10:26:25.073092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:25.330348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
14.7 1
20.0%
17.9 1
20.0%
11.1 1
20.0%
18.4 1
20.0%
21.6 1
20.0%

Most occurring characters

ValueCountFrequency (%)
1 7
35.0%
. 5
25.0%
4 2
 
10.0%
7 2
 
10.0%
9 1
 
5.0%
8 1
 
5.0%
2 1
 
5.0%
6 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7
35.0%
. 5
25.0%
4 2
 
10.0%
7 2
 
10.0%
9 1
 
5.0%
8 1
 
5.0%
2 1
 
5.0%
6 1
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7
35.0%
. 5
25.0%
4 2
 
10.0%
7 2
 
10.0%
9 1
 
5.0%
8 1
 
5.0%
2 1
 
5.0%
6 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7
35.0%
. 5
25.0%
4 2
 
10.0%
7 2
 
10.0%
9 1
 
5.0%
8 1
 
5.0%
2 1
 
5.0%
6 1
 
5.0%

FGC-FGC_GSD_Zone
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)40.0%
Missing15
Missing (%)75.0%
Memory size1.2 KiB
2.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 3
 
15.0%
1.0 2
 
10.0%
(Missing) 15
75.0%

Length

2024-10-11T10:26:25.576037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:25.795334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3
60.0%
1.0 2
40.0%

Most occurring characters

ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 5
33.3%
0 5
33.3%
2 3
20.0%
1 2
 
13.3%

FGC-FGC_PU
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)46.2%
Missing7
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum0
Maximum20
Zeros6
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:25.980164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q36
95-th percentile12.2
Maximum20
Range20
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.6273143
Coefficient of variation (CV)1.4068286
Kurtosis5.3218655
Mean4
Median Absolute Deviation (MAD)2
Skewness2.0824606
Sum52
Variance31.666667
MonotonicityNot monotonic
2024-10-11T10:26:26.218092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 6
30.0%
5 2
 
10.0%
7 2
 
10.0%
6 1
 
5.0%
2 1
 
5.0%
20 1
 
5.0%
(Missing) 7
35.0%
ValueCountFrequency (%)
0 6
30.0%
2 1
 
5.0%
5 2
 
10.0%
6 1
 
5.0%
7 2
 
10.0%
20 1
 
5.0%
ValueCountFrequency (%)
20 1
 
5.0%
7 2
 
10.0%
6 1
 
5.0%
5 2
 
10.0%
2 1
 
5.0%
0 6
30.0%

FGC-FGC_PU_Zone
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)15.4%
Missing7
Missing (%)35.0%
Memory size1.3 KiB
0.0
11 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11
55.0%
1.0 2
 
10.0%
(Missing) 7
35.0%

Length

2024-10-11T10:26:26.467365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:26.692911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11
84.6%
1.0 2
 
15.4%

Most occurring characters

ValueCountFrequency (%)
0 24
61.5%
. 13
33.3%
1 2
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 24
61.5%
. 13
33.3%
1 2
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 24
61.5%
. 13
33.3%
1 2
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 24
61.5%
. 13
33.3%
1 2
 
5.1%

FGC-FGC_SRL
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)61.5%
Missing7
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean7.5
Minimum0
Maximum12
Zeros2
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:26.877974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median8
Q310.5
95-th percentile11.4
Maximum12
Range12
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation4
Coefficient of variation (CV)0.53333333
Kurtosis0.030144709
Mean7.5
Median Absolute Deviation (MAD)2.5
Skewness-1.0029297
Sum97.5
Variance16
MonotonicityNot monotonic
2024-10-11T10:26:27.088029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
7 3
15.0%
11 2
 
10.0%
10 2
 
10.0%
0 2
 
10.0%
8 1
 
5.0%
12 1
 
5.0%
10.5 1
 
5.0%
4 1
 
5.0%
(Missing) 7
35.0%
ValueCountFrequency (%)
0 2
10.0%
4 1
 
5.0%
7 3
15.0%
8 1
 
5.0%
10 2
10.0%
10.5 1
 
5.0%
11 2
10.0%
12 1
 
5.0%
ValueCountFrequency (%)
12 1
 
5.0%
11 2
10.0%
10.5 1
 
5.0%
10 2
10.0%
8 1
 
5.0%
7 3
15.0%
4 1
 
5.0%
0 2
10.0%

FGC-FGC_SRL_Zone
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)15.4%
Missing7
Missing (%)35.0%
Memory size1.3 KiB
1.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 7
35.0%
0.0 6
30.0%
(Missing) 7
35.0%

Length

2024-10-11T10:26:27.341850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:27.572142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 7
53.8%
0.0 6
46.2%

Most occurring characters

ValueCountFrequency (%)
0 19
48.7%
. 13
33.3%
1 7
 
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19
48.7%
. 13
33.3%
1 7
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19
48.7%
. 13
33.3%
1 7
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19
48.7%
. 13
33.3%
1 7
 
17.9%

FGC-FGC_SRR
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)69.2%
Missing7
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean7.9615385
Minimum0
Maximum15
Zeros2
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:27.784215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median9.5
Q311
95-th percentile12.6
Maximum15
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.4368792
Coefficient of variation (CV)0.55728918
Kurtosis0.0037031813
Mean7.9615385
Median Absolute Deviation (MAD)1.5
Skewness-0.70513368
Sum103.5
Variance19.685897
MonotonicityNot monotonic
2024-10-11T10:26:27.990912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11 3
15.0%
10 2
 
10.0%
0 2
 
10.0%
6 1
 
5.0%
7 1
 
5.0%
9.5 1
 
5.0%
9 1
 
5.0%
15 1
 
5.0%
4 1
 
5.0%
(Missing) 7
35.0%
ValueCountFrequency (%)
0 2
10.0%
4 1
 
5.0%
6 1
 
5.0%
7 1
 
5.0%
9 1
 
5.0%
9.5 1
 
5.0%
10 2
10.0%
11 3
15.0%
15 1
 
5.0%
ValueCountFrequency (%)
15 1
 
5.0%
11 3
15.0%
10 2
10.0%
9.5 1
 
5.0%
9 1
 
5.0%
7 1
 
5.0%
6 1
 
5.0%
4 1
 
5.0%
0 2
10.0%

FGC-FGC_SRR_Zone
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)15.4%
Missing7
Missing (%)35.0%
Memory size1.3 KiB
1.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 8
40.0%
0.0 5
25.0%
(Missing) 7
35.0%

Length

2024-10-11T10:26:28.285949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:28.664437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 8
61.5%
0.0 5
38.5%

Most occurring characters

ValueCountFrequency (%)
0 18
46.2%
. 13
33.3%
1 8
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18
46.2%
. 13
33.3%
1 8
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18
46.2%
. 13
33.3%
1 8
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18
46.2%
. 13
33.3%
1 8
20.5%

FGC-FGC_TL
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)76.9%
Missing7
Missing (%)35.0%
Infinite0
Infinite (%)0.0%
Mean7.9615385
Minimum3
Maximum12.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:28.945153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.6
Q16
median7
Q311
95-th percentile12.2
Maximum12.5
Range9.5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1521259
Coefficient of variation (CV)0.39591919
Kurtosis-1.1490605
Mean7.9615385
Median Absolute Deviation (MAD)2
Skewness0.10654728
Sum103.5
Variance9.9358974
MonotonicityNot monotonic
2024-10-11T10:26:29.282637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 3
15.0%
12 2
 
10.0%
6 1
 
5.0%
3 1
 
5.0%
5 1
 
5.0%
8 1
 
5.0%
11 1
 
5.0%
4 1
 
5.0%
12.5 1
 
5.0%
9 1
 
5.0%
(Missing) 7
35.0%
ValueCountFrequency (%)
3 1
 
5.0%
4 1
 
5.0%
5 1
 
5.0%
6 1
 
5.0%
7 3
15.0%
8 1
 
5.0%
9 1
 
5.0%
11 1
 
5.0%
12 2
10.0%
12.5 1
 
5.0%
ValueCountFrequency (%)
12.5 1
 
5.0%
12 2
10.0%
11 1
 
5.0%
9 1
 
5.0%
8 1
 
5.0%
7 3
15.0%
6 1
 
5.0%
5 1
 
5.0%
4 1
 
5.0%
3 1
 
5.0%

FGC-FGC_TL_Zone
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)15.4%
Missing7
Missing (%)35.0%
Memory size1.3 KiB
1.0
0.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters39
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 9
45.0%
0.0 4
20.0%
(Missing) 7
35.0%

Length

2024-10-11T10:26:29.697301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:30.107283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 9
69.2%
0.0 4
30.8%

Most occurring characters

ValueCountFrequency (%)
0 17
43.6%
. 13
33.3%
1 9
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 17
43.6%
. 13
33.3%
1 9
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 17
43.6%
. 13
33.3%
1 9
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 17
43.6%
. 13
33.3%
1 9
23.1%

BIA-Season
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)37.5%
Missing12
Missing (%)60.0%
Memory size1.3 KiB
Fall
Winter
Summer

Length

Max length6
Median length6
Mean length5.25
Min length4

Characters and Unicode

Total characters42
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFall
2nd rowWinter
3rd rowSummer
4th rowSummer
5th rowFall

Common Values

ValueCountFrequency (%)
Fall 3
 
15.0%
Winter 3
 
15.0%
Summer 2
 
10.0%
(Missing) 12
60.0%

Length

2024-10-11T10:26:30.518121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:30.964576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fall 3
37.5%
winter 3
37.5%
summer 2
25.0%

Most occurring characters

ValueCountFrequency (%)
l 6
14.3%
e 5
11.9%
r 5
11.9%
m 4
9.5%
F 3
7.1%
a 3
7.1%
W 3
7.1%
i 3
7.1%
n 3
7.1%
t 3
7.1%
Other values (2) 4
9.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 6
14.3%
e 5
11.9%
r 5
11.9%
m 4
9.5%
F 3
7.1%
a 3
7.1%
W 3
7.1%
i 3
7.1%
n 3
7.1%
t 3
7.1%
Other values (2) 4
9.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 6
14.3%
e 5
11.9%
r 5
11.9%
m 4
9.5%
F 3
7.1%
a 3
7.1%
W 3
7.1%
i 3
7.1%
n 3
7.1%
t 3
7.1%
Other values (2) 4
9.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 6
14.3%
e 5
11.9%
r 5
11.9%
m 4
9.5%
F 3
7.1%
a 3
7.1%
W 3
7.1%
i 3
7.1%
n 3
7.1%
t 3
7.1%
Other values (2) 4
9.5%

BIA-BIA_Activity_Level_num
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)37.5%
Missing12
Missing (%)60.0%
Memory size1.2 KiB
2.0
3.0
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)12.5%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 5
25.0%
3.0 2
 
10.0%
5.0 1
 
5.0%
(Missing) 12
60.0%

Length

2024-10-11T10:26:31.347068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:31.718908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 5
62.5%
3.0 2
 
25.0%
5.0 1
 
12.5%

Most occurring characters

ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
2 5
20.8%
3 2
 
8.3%
5 1
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
2 5
20.8%
3 2
 
8.3%
5 1
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
2 5
20.8%
3 2
 
8.3%
5 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
2 5
20.8%
3 2
 
8.3%
5 1
 
4.2%

BIA-BIA_BMC
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean3.63636
Minimum2.57949
Maximum5.08025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:32.116413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.57949
5-th percentile2.610661
Q12.7299
median3.81231
Q34.125535
95-th percentile4.8177885
Maximum5.08025
Range2.50076
Interquartile range (IQR)1.395635

Descriptive statistics

Standard deviation0.8980872
Coefficient of variation (CV)0.24697423
Kurtosis-1.024571
Mean3.63636
Median Absolute Deviation (MAD)0.790005
Skewness0.17049005
Sum29.09088
Variance0.80656063
MonotonicityNot monotonic
2024-10-11T10:26:32.570378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2.66855 1
 
5.0%
2.57949 1
 
5.0%
3.84191 1
 
5.0%
4.33036 1
 
5.0%
3.78271 1
 
5.0%
4.05726 1
 
5.0%
5.08025 1
 
5.0%
2.75035 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
2.57949 1
5.0%
2.66855 1
5.0%
2.75035 1
5.0%
3.78271 1
5.0%
3.84191 1
5.0%
4.05726 1
5.0%
4.33036 1
5.0%
5.08025 1
5.0%
ValueCountFrequency (%)
5.08025 1
5.0%
4.33036 1
5.0%
4.05726 1
5.0%
3.84191 1
5.0%
3.78271 1
5.0%
2.75035 1
5.0%
2.66855 1
5.0%
2.57949 1
5.0%

BIA-BIA_BMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean19.284788
Minimum14.0371
Maximum30.1865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:32.918385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14.0371
5-th percentile15.0262
Q116.875175
median17.78405
Q320.017525
95-th percentile26.999715
Maximum30.1865
Range16.1494
Interquartile range (IQR)3.14235

Descriptive statistics

Standard deviation4.8760772
Coefficient of variation (CV)0.25284578
Kurtosis4.1845715
Mean19.284788
Median Absolute Deviation (MAD)1.3999
Skewness1.8397185
Sum154.2783
Variance23.776129
MonotonicityNot monotonic
2024-10-11T10:26:33.367144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
16.8792 1
 
5.0%
14.0371 1
 
5.0%
18.2943 1
 
5.0%
30.1865 1
 
5.0%
19.6629 1
 
5.0%
16.8631 1
 
5.0%
21.0814 1
 
5.0%
17.2738 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
14.0371 1
5.0%
16.8631 1
5.0%
16.8792 1
5.0%
17.2738 1
5.0%
18.2943 1
5.0%
19.6629 1
5.0%
21.0814 1
5.0%
30.1865 1
5.0%
ValueCountFrequency (%)
30.1865 1
5.0%
21.0814 1
5.0%
19.6629 1
5.0%
18.2943 1
5.0%
17.2738 1
5.0%
16.8792 1
5.0%
16.8631 1
5.0%
14.0371 1
5.0%

BIA-BIA_BMR
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean1111.248
Minimum932.498
Maximum1330.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:33.854085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum932.498
5-th percentile933.9533
Q1986.4665
median1133.645
Q31194.895
95-th percentile1298.9415
Maximum1330.97
Range398.472
Interquartile range (IQR)208.4285

Descriptive statistics

Standard deviation143.72488
Coefficient of variation (CV)0.12933646
Kurtosis-1.1147856
Mean1111.248
Median Absolute Deviation (MAD)118.195
Skewness0.049059736
Sum8889.984
Variance20656.841
MonotonicityNot monotonic
2024-10-11T10:26:34.077672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
932.498 1
 
5.0%
936.656 1
 
5.0%
1131.43 1
 
5.0%
1330.97 1
 
5.0%
1135.86 1
 
5.0%
1180.04 1
 
5.0%
1239.46 1
 
5.0%
1003.07 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
932.498 1
5.0%
936.656 1
5.0%
1003.07 1
5.0%
1131.43 1
5.0%
1135.86 1
5.0%
1180.04 1
5.0%
1239.46 1
5.0%
1330.97 1
5.0%
ValueCountFrequency (%)
1330.97 1
5.0%
1239.46 1
5.0%
1180.04 1
5.0%
1135.86 1
5.0%
1131.43 1
5.0%
1003.07 1
5.0%
936.656 1
5.0%
932.498 1
5.0%

BIA-BIA_DEE
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean1886.9125
Minimum1492
Maximum2974.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:34.299831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1492
5-th percentile1494.3275
Q11503.12
median1852.72
Q31941.6925
95-th percentile2632.319
Maximum2974.71
Range1482.71
Interquartile range (IQR)438.5725

Descriptive statistics

Standard deviation486.14094
Coefficient of variation (CV)0.2576383
Kurtosis4.1161789
Mean1886.9125
Median Absolute Deviation (MAD)245.92
Skewness1.8439436
Sum15095.3
Variance236333.01
MonotonicityNot monotonic
2024-10-11T10:26:34.504107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1492 1
 
5.0%
1498.65 1
 
5.0%
1923.44 1
 
5.0%
1996.45 1
 
5.0%
1817.38 1
 
5.0%
1888.06 1
 
5.0%
2974.71 1
 
5.0%
1504.61 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
1492 1
5.0%
1498.65 1
5.0%
1504.61 1
5.0%
1817.38 1
5.0%
1888.06 1
5.0%
1923.44 1
5.0%
1996.45 1
5.0%
2974.71 1
5.0%
ValueCountFrequency (%)
2974.71 1
5.0%
1996.45 1
5.0%
1923.44 1
5.0%
1888.06 1
5.0%
1817.38 1
5.0%
1504.61 1
5.0%
1498.65 1
5.0%
1492 1
5.0%

BIA-BIA_ECW
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean16.681051
Minimum6.01993
Maximum30.2124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:34.747922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.01993
5-th percentile6.8025475
Q113.423195
median15.96
Q320.450875
95-th percentile27.31706
Maximum30.2124
Range24.19247
Interquartile range (IQR)7.02768

Descriptive statistics

Standard deviation7.6511278
Coefficient of variation (CV)0.4586718
Kurtosis0.33937395
Mean16.681051
Median Absolute Deviation (MAD)4.98725
Skewness0.35865403
Sum133.44841
Variance58.539756
MonotonicityNot monotonic
2024-10-11T10:26:34.963893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8.25598 1
 
5.0%
6.01993 1
 
5.0%
15.5925 1
 
5.0%
30.2124 1
 
5.0%
16.3275 1
 
5.0%
21.94 1
 
5.0%
19.9545 1
 
5.0%
15.1456 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
6.01993 1
5.0%
8.25598 1
5.0%
15.1456 1
5.0%
15.5925 1
5.0%
16.3275 1
5.0%
19.9545 1
5.0%
21.94 1
5.0%
30.2124 1
5.0%
ValueCountFrequency (%)
30.2124 1
5.0%
21.94 1
5.0%
19.9545 1
5.0%
16.3275 1
5.0%
15.5925 1
5.0%
15.1456 1
5.0%
8.25598 1
5.0%
6.01993 1
5.0%

BIA-BIA_FFM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean60.625613
Minimum41.5862
Maximum84.0285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:35.195577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum41.5862
5-th percentile41.741215
Q147.334825
median63.01135
Q369.5351
95-th percentile80.61733
Maximum84.0285
Range42.4423
Interquartile range (IQR)22.200275

Descriptive statistics

Standard deviation15.308597
Coefficient of variation (CV)0.25251038
Kurtosis-1.1148406
Mean60.625613
Median Absolute Deviation (MAD)12.58945
Skewness0.049020042
Sum485.0049
Variance234.35313
MonotonicityNot monotonic
2024-10-11T10:26:35.417606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
41.5862 1
 
5.0%
42.0291 1
 
5.0%
62.7757 1
 
5.0%
84.0285 1
 
5.0%
63.247 1
 
5.0%
67.9527 1
 
5.0%
74.2823 1
 
5.0%
49.1034 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
41.5862 1
5.0%
42.0291 1
5.0%
49.1034 1
5.0%
62.7757 1
5.0%
63.247 1
5.0%
67.9527 1
5.0%
74.2823 1
5.0%
84.0285 1
5.0%
ValueCountFrequency (%)
84.0285 1
5.0%
74.2823 1
5.0%
67.9527 1
5.0%
63.247 1
5.0%
62.7757 1
5.0%
49.1034 1
5.0%
42.0291 1
5.0%
41.5862 1
5.0%

BIA-BIA_FFMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean14.432937
Minimum12.8254
Maximum16.6877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:35.632236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12.8254
5-th percentile13.09973
Q113.765575
median14.0819
Q314.939925
95-th percentile16.3279
Maximum16.6877
Range3.8623
Interquartile range (IQR)1.17435

Descriptive statistics

Standard deviation1.2275434
Coefficient of variation (CV)0.085051528
Kurtosis0.44959114
Mean14.432937
Median Absolute Deviation (MAD)0.5454
Skewness0.85425063
Sum115.4635
Variance1.5068628
MonotonicityNot monotonic
2024-10-11T10:26:35.864772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
13.8177 1
 
5.0%
12.8254 1
 
5.0%
14.074 1
 
5.0%
16.6877 1
 
5.0%
14.7 1
 
5.0%
13.6092 1
 
5.0%
15.6597 1
 
5.0%
14.0898 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
12.8254 1
5.0%
13.6092 1
5.0%
13.8177 1
5.0%
14.074 1
5.0%
14.0898 1
5.0%
14.7 1
5.0%
15.6597 1
5.0%
16.6877 1
5.0%
ValueCountFrequency (%)
16.6877 1
5.0%
15.6597 1
5.0%
14.7 1
5.0%
14.0898 1
5.0%
14.074 1
5.0%
13.8177 1
5.0%
13.6092 1
5.0%
12.8254 1
5.0%

BIA-BIA_FMI
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean4.8518575
Minimum1.21172
Maximum13.4988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:36.093918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.21172
5-th percentile1.8591185
Q13.15341
median3.73714
Q35.077595
95-th percentile10.671797
Maximum13.4988
Range12.28708
Interquartile range (IQR)1.924185

Descriptive statistics

Standard deviation3.7282032
Coefficient of variation (CV)0.7684074
Kurtosis5.3768205
Mean4.8518575
Median Absolute Deviation (MAD)0.95074
Skewness2.1457753
Sum38.81486
Variance13.899499
MonotonicityNot monotonic
2024-10-11T10:26:36.310586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3.06143 1
 
5.0%
1.21172 1
 
5.0%
4.22033 1
 
5.0%
13.4988 1
 
5.0%
4.96291 1
 
5.0%
3.25395 1
 
5.0%
5.42165 1
 
5.0%
3.18407 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
1.21172 1
5.0%
3.06143 1
5.0%
3.18407 1
5.0%
3.25395 1
5.0%
4.22033 1
5.0%
4.96291 1
5.0%
5.42165 1
5.0%
13.4988 1
5.0%
ValueCountFrequency (%)
13.4988 1
5.0%
5.42165 1
5.0%
4.96291 1
5.0%
4.22033 1
5.0%
3.25395 1
5.0%
3.18407 1
5.0%
3.06143 1
5.0%
1.21172 1
5.0%

BIA-BIA_Fat
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean21.79939
Minimum3.97085
Maximum67.9715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:36.519131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.97085
5-th percentile5.805872
Q110.625893
median17.53585
Q322.444175
95-th percentile53.18267
Maximum67.9715
Range64.00065
Interquartile range (IQR)11.818283

Descriptive statistics

Standard deviation19.920902
Coefficient of variation (CV)0.9138284
Kurtosis5.3004522
Mean21.79939
Median Absolute Deviation (MAD)7.31055
Skewness2.1542391
Sum174.39512
Variance396.84233
MonotonicityNot monotonic
2024-10-11T10:26:36.745044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
9.21377 1
 
5.0%
3.97085 1
 
5.0%
18.8243 1
 
5.0%
67.9715 1
 
5.0%
21.353 1
 
5.0%
16.2474 1
 
5.0%
25.7177 1
 
5.0%
11.0966 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
3.97085 1
5.0%
9.21377 1
5.0%
11.0966 1
5.0%
16.2474 1
5.0%
18.8243 1
5.0%
21.353 1
5.0%
25.7177 1
5.0%
67.9715 1
5.0%
ValueCountFrequency (%)
67.9715 1
5.0%
25.7177 1
5.0%
21.353 1
5.0%
18.8243 1
5.0%
16.2474 1
5.0%
11.0966 1
5.0%
9.21377 1
5.0%
3.97085 1
5.0%

BIA-BIA_Frame_num
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)25.0%
Missing12
Missing (%)60.0%
Memory size1.2 KiB
2.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 5
25.0%
1.0 3
 
15.0%
(Missing) 12
60.0%

Length

2024-10-11T10:26:37.025627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:37.267570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 5
62.5%
1.0 3
37.5%

Most occurring characters

ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
2 5
20.8%
1 3
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
2 5
20.8%
1 3
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
2 5
20.8%
1 3
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 8
33.3%
0 8
33.3%
2 5
20.8%
1 3
 
12.5%

BIA-BIA_ICW
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean28.48675
Minimum21.0352
Maximum36.0572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:37.452645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21.0352
5-th percentile21.93925
Q124.230725
median29.4704
Q331.398725
95-th percentile34.957115
Maximum36.0572
Range15.022
Interquartile range (IQR)7.168

Descriptive statistics

Standard deviation5.099449
Coefficient of variation (CV)0.17901126
Kurtosis-1.031294
Mean28.48675
Median Absolute Deviation (MAD)4.2396
Skewness-0.077596171
Sum227.894
Variance26.00438
MonotonicityNot monotonic
2024-10-11T10:26:37.648153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
24.4349 1
 
5.0%
21.0352 1
 
5.0%
30.4041 1
 
5.0%
32.9141 1
 
5.0%
30.8936 1
 
5.0%
28.5367 1
 
5.0%
36.0572 1
 
5.0%
23.6182 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
21.0352 1
5.0%
23.6182 1
5.0%
24.4349 1
5.0%
28.5367 1
5.0%
30.4041 1
5.0%
30.8936 1
5.0%
32.9141 1
5.0%
36.0572 1
5.0%
ValueCountFrequency (%)
36.0572 1
5.0%
32.9141 1
5.0%
30.8936 1
5.0%
30.4041 1
5.0%
28.5367 1
5.0%
24.4349 1
5.0%
23.6182 1
5.0%
21.0352 1
5.0%

BIA-BIA_LDM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean15.457795
Minimum8.89536
Maximum20.902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:37.871876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.89536
5-th percentile9.400844
Q113.8154
median16.40245
Q317.674625
95-th percentile19.980975
Maximum20.902
Range12.00664
Interquartile range (IQR)3.859225

Descriptive statistics

Standard deviation4.0211527
Coefficient of variation (CV)0.26013754
Kurtosis-0.30806729
Mean15.457795
Median Absolute Deviation (MAD)1.64825
Skewness-0.65174517
Sum123.66236
Variance16.169669
MonotonicityNot monotonic
2024-10-11T10:26:38.079931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8.89536 1
 
5.0%
14.974 1
 
5.0%
16.779 1
 
5.0%
20.902 1
 
5.0%
16.0259 1
 
5.0%
17.476 1
 
5.0%
18.2705 1
 
5.0%
10.3396 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
8.89536 1
5.0%
10.3396 1
5.0%
14.974 1
5.0%
16.0259 1
5.0%
16.779 1
5.0%
17.476 1
5.0%
18.2705 1
5.0%
20.902 1
5.0%
ValueCountFrequency (%)
20.902 1
5.0%
18.2705 1
5.0%
17.476 1
5.0%
16.779 1
5.0%
16.0259 1
5.0%
14.974 1
5.0%
10.3396 1
5.0%
8.89536 1
5.0%

BIA-BIA_LST
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean56.989275
Minimum38.9177
Maximum79.6982
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:38.286737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum38.9177
5-th percentile39.1039
Q144.62725
median59.19905
Q365.22205
95-th percentile76.02453
Maximum79.6982
Range40.7805
Interquartile range (IQR)20.5948

Descriptive statistics

Standard deviation14.490362
Coefficient of variation (CV)0.25426472
Kurtosis-0.97580566
Mean56.989275
Median Absolute Deviation (MAD)11.42445
Skewness0.086606775
Sum455.9142
Variance209.97059
MonotonicityNot monotonic
2024-10-11T10:26:38.499647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
38.9177 1
 
5.0%
39.4497 1
 
5.0%
58.9338 1
 
5.0%
79.6982 1
 
5.0%
59.4643 1
 
5.0%
63.8954 1
 
5.0%
69.202 1
 
5.0%
46.3531 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
38.9177 1
5.0%
39.4497 1
5.0%
46.3531 1
5.0%
58.9338 1
5.0%
59.4643 1
5.0%
63.8954 1
5.0%
69.202 1
5.0%
79.6982 1
5.0%
ValueCountFrequency (%)
79.6982 1
5.0%
69.202 1
5.0%
63.8954 1
5.0%
59.4643 1
5.0%
58.9338 1
5.0%
46.3531 1
5.0%
39.4497 1
5.0%
38.9177 1
5.0%

BIA-BIA_SMM
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean25.985962
Minimum15.4107
Maximum36.2232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:38.735971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum15.4107
5-th percentile16.85641
Q119.801775
median26.33775
Q330.4211
95-th percentile35.92822
Maximum36.2232
Range20.8125
Interquartile range (IQR)10.619325

Descriptive statistics

Standard deviation7.4797991
Coefficient of variation (CV)0.28783999
Kurtosis-1.1268635
Mean25.985962
Median Absolute Deviation (MAD)6.6228
Skewness0.11224019
Sum207.8877
Variance55.947395
MonotonicityNot monotonic
2024-10-11T10:26:38.976803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
19.5413 1
 
5.0%
15.4107 1
 
5.0%
26.4798 1
 
5.0%
35.3804 1
 
5.0%
26.1957 1
 
5.0%
28.768 1
 
5.0%
36.2232 1
 
5.0%
19.8886 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
15.4107 1
5.0%
19.5413 1
5.0%
19.8886 1
5.0%
26.1957 1
5.0%
26.4798 1
5.0%
28.768 1
5.0%
35.3804 1
5.0%
36.2232 1
5.0%
ValueCountFrequency (%)
36.2232 1
5.0%
35.3804 1
5.0%
28.768 1
5.0%
26.4798 1
5.0%
26.1957 1
5.0%
19.8886 1
5.0%
19.5413 1
5.0%
15.4107 1
5.0%

BIA-BIA_TBW
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing12
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean45.167825
Minimum27.0552
Maximum63.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:39.176912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum27.0552
5-th percentile29.027695
Q137.245575
median46.60885
Q351.860475
95-th percentile60.636355
Maximum63.1265
Range36.0713
Interquartile range (IQR)14.6149

Descriptive statistics

Standard deviation11.94
Coefficient of variation (CV)0.26434746
Kurtosis-0.64557314
Mean45.167825
Median Absolute Deviation (MAD)8.624
Skewness-0.10308024
Sum361.3426
Variance142.56359
MonotonicityNot monotonic
2024-10-11T10:26:39.378874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
32.6909 1
 
5.0%
27.0552 1
 
5.0%
45.9966 1
 
5.0%
63.1265 1
 
5.0%
47.2211 1
 
5.0%
50.4767 1
 
5.0%
56.0118 1
 
5.0%
38.7638 1
 
5.0%
(Missing) 12
60.0%
ValueCountFrequency (%)
27.0552 1
5.0%
32.6909 1
5.0%
38.7638 1
5.0%
45.9966 1
5.0%
47.2211 1
5.0%
50.4767 1
5.0%
56.0118 1
5.0%
63.1265 1
5.0%
ValueCountFrequency (%)
63.1265 1
5.0%
56.0118 1
5.0%
50.4767 1
5.0%
47.2211 1
5.0%
45.9966 1
5.0%
38.7638 1
5.0%
32.6909 1
5.0%
27.0552 1
5.0%

PAQ_A-Season
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing19
Missing (%)95.0%
Memory size799.0 B
2024-10-11T10:26:39.648268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters6
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowSummer
ValueCountFrequency (%)
summer 1
100.0%
2024-10-11T10:26:40.140015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 2
33.3%
S 1
16.7%
u 1
16.7%
e 1
16.7%
r 1
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 2
33.3%
S 1
16.7%
u 1
16.7%
e 1
16.7%
r 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 2
33.3%
S 1
16.7%
u 1
16.7%
e 1
16.7%
r 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 2
33.3%
S 1
16.7%
u 1
16.7%
e 1
16.7%
r 1
16.7%

PAQ_A-PAQ_A_Total
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing19
Missing (%)95.0%
Memory size1.2 KiB
1.04

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.04

Common Values

ValueCountFrequency (%)
1.04 1
 
5.0%
(Missing) 19
95.0%

Length

2024-10-11T10:26:40.401745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:40.618565image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.04 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
25.0%
. 1
25.0%
0 1
25.0%
4 1
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1
25.0%
. 1
25.0%
0 1
25.0%
4 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1
25.0%
. 1
25.0%
0 1
25.0%
4 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1
25.0%
. 1
25.0%
0 1
25.0%
4 1
25.0%

PAQ_C-Season
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)44.4%
Missing11
Missing (%)55.0%
Memory size1.3 KiB
Fall
Winter
Spring
Summer

Length

Max length6
Median length6
Mean length5.3333333
Min length4

Characters and Unicode

Total characters48
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)11.1%

Sample

1st rowFall
2nd rowSummer
3rd rowWinter
4th rowSpring
5th rowWinter

Common Values

ValueCountFrequency (%)
Fall 3
 
15.0%
Winter 3
 
15.0%
Spring 2
 
10.0%
Summer 1
 
5.0%
(Missing) 11
55.0%

Length

2024-10-11T10:26:40.837629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:41.151777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fall 3
33.3%
winter 3
33.3%
spring 2
22.2%
summer 1
 
11.1%

Most occurring characters

ValueCountFrequency (%)
l 6
12.5%
r 6
12.5%
i 5
10.4%
n 5
10.4%
e 4
8.3%
F 3
6.2%
a 3
6.2%
W 3
6.2%
t 3
6.2%
S 3
6.2%
Other values (4) 7
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 6
12.5%
r 6
12.5%
i 5
10.4%
n 5
10.4%
e 4
8.3%
F 3
6.2%
a 3
6.2%
W 3
6.2%
t 3
6.2%
S 3
6.2%
Other values (4) 7
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 6
12.5%
r 6
12.5%
i 5
10.4%
n 5
10.4%
e 4
8.3%
F 3
6.2%
a 3
6.2%
W 3
6.2%
t 3
6.2%
S 3
6.2%
Other values (4) 7
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 6
12.5%
r 6
12.5%
i 5
10.4%
n 5
10.4%
e 4
8.3%
F 3
6.2%
a 3
6.2%
W 3
6.2%
t 3
6.2%
S 3
6.2%
Other values (4) 7
14.6%

PAQ_C-PAQ_C_Total
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing11
Missing (%)55.0%
Infinite0
Infinite (%)0.0%
Mean2.3723333
Minimum1.1
Maximum4.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:41.378447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.148
Q11.27
median2.34
Q33.02
95-th percentile3.934
Maximum4.11
Range3.01
Interquartile range (IQR)1.75

Descriptive statistics

Standard deviation1.0800991
Coefficient of variation (CV)0.45528976
Kurtosis-1.0305984
Mean2.3723333
Median Absolute Deviation (MAD)1.07
Skewness0.35286174
Sum21.351
Variance1.166614
MonotonicityNot monotonic
2024-10-11T10:26:41.587407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2.34 1
 
5.0%
2.17 1
 
5.0%
2.451 1
 
5.0%
4.11 1
 
5.0%
3.67 1
 
5.0%
1.27 1
 
5.0%
1.1 1
 
5.0%
3.02 1
 
5.0%
1.22 1
 
5.0%
(Missing) 11
55.0%
ValueCountFrequency (%)
1.1 1
5.0%
1.22 1
5.0%
1.27 1
5.0%
2.17 1
5.0%
2.34 1
5.0%
2.451 1
5.0%
3.02 1
5.0%
3.67 1
5.0%
4.11 1
5.0%
ValueCountFrequency (%)
4.11 1
5.0%
3.67 1
5.0%
3.02 1
5.0%
2.451 1
5.0%
2.34 1
5.0%
2.17 1
5.0%
1.27 1
5.0%
1.22 1
5.0%
1.1 1
5.0%

SDS-Season
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)40.0%
Missing10
Missing (%)50.0%
Memory size1.3 KiB
Fall
Winter
Summer
Spring

Length

Max length6
Median length6
Mean length5.4
Min length4

Characters and Unicode

Total characters54
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFall
2nd rowFall
3rd rowSummer
4th rowSummer
5th rowWinter

Common Values

ValueCountFrequency (%)
Fall 3
 
15.0%
Winter 3
 
15.0%
Summer 2
 
10.0%
Spring 2
 
10.0%
(Missing) 10
50.0%

Length

2024-10-11T10:26:41.850704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:42.135923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fall 3
30.0%
winter 3
30.0%
summer 2
20.0%
spring 2
20.0%

Most occurring characters

ValueCountFrequency (%)
r 7
13.0%
l 6
11.1%
i 5
9.3%
n 5
9.3%
e 5
9.3%
S 4
7.4%
m 4
7.4%
F 3
 
5.6%
a 3
 
5.6%
W 3
 
5.6%
Other values (4) 9
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 7
13.0%
l 6
11.1%
i 5
9.3%
n 5
9.3%
e 5
9.3%
S 4
7.4%
m 4
7.4%
F 3
 
5.6%
a 3
 
5.6%
W 3
 
5.6%
Other values (4) 9
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 7
13.0%
l 6
11.1%
i 5
9.3%
n 5
9.3%
e 5
9.3%
S 4
7.4%
m 4
7.4%
F 3
 
5.6%
a 3
 
5.6%
W 3
 
5.6%
Other values (4) 9
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 7
13.0%
l 6
11.1%
i 5
9.3%
n 5
9.3%
e 5
9.3%
S 4
7.4%
m 4
7.4%
F 3
 
5.6%
a 3
 
5.6%
W 3
 
5.6%
Other values (4) 9
16.7%

SDS-SDS_Total_Raw
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing10
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean36.8
Minimum27
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:42.350665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile28.8
Q133.5
median37.5
Q339.75
95-th percentile44.2
Maximum46
Range19
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation5.5337349
Coefficient of variation (CV)0.15037323
Kurtosis-0.010193581
Mean36.8
Median Absolute Deviation (MAD)3.5
Skewness-0.19061078
Sum368
Variance30.622222
MonotonicityNot monotonic
2024-10-11T10:26:42.564717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
46 1
 
5.0%
38 1
 
5.0%
31 1
 
5.0%
40 1
 
5.0%
27 1
 
5.0%
42 1
 
5.0%
33 1
 
5.0%
35 1
 
5.0%
37 1
 
5.0%
39 1
 
5.0%
(Missing) 10
50.0%
ValueCountFrequency (%)
27 1
5.0%
31 1
5.0%
33 1
5.0%
35 1
5.0%
37 1
5.0%
38 1
5.0%
39 1
5.0%
40 1
5.0%
42 1
5.0%
46 1
5.0%
ValueCountFrequency (%)
46 1
5.0%
42 1
5.0%
40 1
5.0%
39 1
5.0%
38 1
5.0%
37 1
5.0%
35 1
5.0%
33 1
5.0%
31 1
5.0%
27 1
5.0%

SDS-SDS_Total_T
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing10
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean52.3
Minimum40
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.0 B
2024-10-11T10:26:42.794217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile42.25
Q147.75
median53.5
Q355.75
95-th percentile61.75
Maximum64
Range24
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.0245601
Coefficient of variation (CV)0.13431281
Kurtosis-0.077183349
Mean52.3
Median Absolute Deviation (MAD)4.5
Skewness-0.17348338
Sum523
Variance49.344444
MonotonicityNot monotonic
2024-10-11T10:26:43.030243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
64 1
 
5.0%
54 1
 
5.0%
45 1
 
5.0%
56 1
 
5.0%
40 1
 
5.0%
59 1
 
5.0%
47 1
 
5.0%
50 1
 
5.0%
53 1
 
5.0%
55 1
 
5.0%
(Missing) 10
50.0%
ValueCountFrequency (%)
40 1
5.0%
45 1
5.0%
47 1
5.0%
50 1
5.0%
53 1
5.0%
54 1
5.0%
55 1
5.0%
56 1
5.0%
59 1
5.0%
64 1
5.0%
ValueCountFrequency (%)
64 1
5.0%
59 1
5.0%
56 1
5.0%
55 1
5.0%
54 1
5.0%
53 1
5.0%
50 1
5.0%
47 1
5.0%
45 1
5.0%
40 1
5.0%

PreInt_EduHx-Season
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)22.2%
Missing2
Missing (%)10.0%
Memory size1.3 KiB
Fall
Spring
Winter
Summer

Length

Max length6
Median length6
Mean length5.3333333
Min length4

Characters and Unicode

Total characters96
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFall
2nd rowSummer
3rd rowSummer
4th rowWinter
5th rowSpring

Common Values

ValueCountFrequency (%)
Fall 6
30.0%
Spring 5
25.0%
Winter 4
20.0%
Summer 3
15.0%
(Missing) 2
 
10.0%

Length

2024-10-11T10:26:43.277321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:43.581662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fall 6
33.3%
spring 5
27.8%
winter 4
22.2%
summer 3
16.7%

Most occurring characters

ValueCountFrequency (%)
l 12
12.5%
r 12
12.5%
i 9
9.4%
n 9
9.4%
S 8
8.3%
e 7
7.3%
F 6
 
6.2%
a 6
 
6.2%
m 6
 
6.2%
p 5
 
5.2%
Other values (4) 16
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 12
12.5%
r 12
12.5%
i 9
9.4%
n 9
9.4%
S 8
8.3%
e 7
7.3%
F 6
 
6.2%
a 6
 
6.2%
m 6
 
6.2%
p 5
 
5.2%
Other values (4) 16
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 12
12.5%
r 12
12.5%
i 9
9.4%
n 9
9.4%
S 8
8.3%
e 7
7.3%
F 6
 
6.2%
a 6
 
6.2%
m 6
 
6.2%
p 5
 
5.2%
Other values (4) 16
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 12
12.5%
r 12
12.5%
i 9
9.4%
n 9
9.4%
S 8
8.3%
e 7
7.3%
F 6
 
6.2%
a 6
 
6.2%
m 6
 
6.2%
p 5
 
5.2%
Other values (4) 16
16.7%

PreInt_EduHx-computerinternet_hoursday
Categorical

HIGH CORRELATION  MISSING 

Distinct4
Distinct (%)25.0%
Missing4
Missing (%)20.0%
Memory size1.3 KiB
2.0
0.0
3.0
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row0.0
3rd row2.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
2.0 6
30.0%
0.0 5
25.0%
3.0 3
15.0%
1.0 2
 
10.0%
(Missing) 4
20.0%

Length

2024-10-11T10:26:43.823144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-11T10:26:44.222810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 6
37.5%
0.0 5
31.2%
3.0 3
18.8%
1.0 2
 
12.5%

Most occurring characters

ValueCountFrequency (%)
0 21
43.8%
. 16
33.3%
2 6
 
12.5%
3 3
 
6.2%
1 2
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21
43.8%
. 16
33.3%
2 6
 
12.5%
3 3
 
6.2%
1 2
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21
43.8%
. 16
33.3%
2 6
 
12.5%
3 3
 
6.2%
1 2
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21
43.8%
. 16
33.3%
2 6
 
12.5%
3 3
 
6.2%
1 2
 
4.2%

Interactions

2024-10-11T10:25:57.421899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:11.334252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:19.911575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:26.346697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:34.781420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:41.087825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:49.318115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:56.070254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:04.091238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-10-11T10:25:40.728736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:49.214539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:56.452113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:26:05.232633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:19.213823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:25.723364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:34.116916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:40.448976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:48.686995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:55.390063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:03.457101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:10.068419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:18.245402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:25.088976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:33.170758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:39.777975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:49.559598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:02.839913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:09.924349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:18.430819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:25.337870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:33.563818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:39.939900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:48.366878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:55.049588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:03.301270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:10.166543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:18.638274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:25.772116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:34.112104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:40.970743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:49.440845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:56.704415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:26:05.477251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:19.454320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:25.920636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:34.311296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:40.666215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:48.888956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:55.619009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:03.670038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:10.271842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:18.453932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:25.298515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:33.374215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:39.977025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:49.754674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:03.204765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:10.153795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:18.719427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:25.567025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:33.771592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:40.139991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:48.615375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:55.273530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:03.528227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:10.376710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:18.839916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:26.022385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:34.315676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:41.205037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:49.718677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:56.959139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:26:05.717444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:19.697041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:26.135073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:34.561707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:40.895707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:49.115707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:22:55.871368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:03.885984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:10.523047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:18.675365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:25.546224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:33.610559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:40.201113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:23:49.969334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:03.596412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:10.385841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:18.963867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:25.817290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:33.989307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:40.347556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:48.846933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:24:55.516258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:03.770760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:10.621171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:19.068236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:26.256734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:34.576126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:41.444471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:49.950130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-11T10:25:57.194990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-11T10:26:44.780015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
BIA-BIA_Activity_Level_numBIA-BIA_BMCBIA-BIA_BMIBIA-BIA_BMRBIA-BIA_DEEBIA-BIA_ECWBIA-BIA_FFMBIA-BIA_FFMIBIA-BIA_FMIBIA-BIA_FatBIA-BIA_Frame_numBIA-BIA_ICWBIA-BIA_LDMBIA-BIA_LSTBIA-BIA_SMMBIA-BIA_TBWBIA-SeasonBasic_Demos-AgeBasic_Demos-Enroll_SeasonBasic_Demos-SexCGAS-CGAS_ScoreCGAS-SeasonFGC-FGC_CUFGC-FGC_CU_ZoneFGC-FGC_GSDFGC-FGC_GSD_ZoneFGC-FGC_GSNDFGC-FGC_GSND_ZoneFGC-FGC_PUFGC-FGC_PU_ZoneFGC-FGC_SRLFGC-FGC_SRL_ZoneFGC-FGC_SRRFGC-FGC_SRR_ZoneFGC-FGC_TLFGC-FGC_TL_ZoneFGC-SeasonFitness_Endurance-Max_StageFitness_Endurance-Time_MinsFitness_Endurance-Time_SecPAQ_C-PAQ_C_TotalPAQ_C-SeasonPhysical-BMIPhysical-Diastolic_BPPhysical-HeartRatePhysical-HeightPhysical-SeasonPhysical-Systolic_BPPhysical-Waist_CircumferencePhysical-WeightPreInt_EduHx-SeasonPreInt_EduHx-computerinternet_hoursdaySDS-SDS_Total_RawSDS-SDS_Total_TSDS-Season
BIA-BIA_Activity_Level_num1.0000.7750.0000.5770.6880.3940.5770.0000.0000.3320.2940.0000.0000.5770.0000.0000.0000.0000.0000.0000.7070.7070.0000.0001.0001.0001.0001.0000.5530.9130.3940.3650.6320.3650.0000.0000.000NaNNaNNaN0.0000.0000.0000.0000.0000.0000.0000.3541.0000.3940.0000.6880.7070.0000.000
BIA-BIA_BMC0.7751.0000.7140.9290.9520.9050.9290.6670.8810.8810.7070.8810.9050.9291.0000.9520.0000.8370.0000.333-0.2000.0000.6230.0001.0001.0001.0001.0000.5400.707-0.1450.3330.0000.3330.6470.0000.000NaNNaNNaN0.3140.0000.429-0.396-0.0360.8330.0000.0361.0000.9050.0000.319-0.314-0.3140.000
BIA-BIA_BMI0.0000.7141.0000.7140.7380.6430.7140.9760.9290.9290.0000.8810.6190.7140.7140.7620.3290.6430.7750.000-0.2000.0000.8020.5771.0001.0001.0001.0000.6140.5770.2770.5770.4150.5770.9220.1490.224NaNNaNNaN0.8861.0000.762-0.414-0.1070.4760.2240.1071.0000.8570.7750.000-0.257-0.2570.000
BIA-BIA_BMR0.5770.9290.7141.0000.9050.9521.0000.6900.9050.9050.4080.8330.9521.0000.9290.9760.0000.9460.0000.408-0.2001.0000.5630.4081.0001.0001.0001.0000.5650.4080.0720.0000.2200.0000.6110.0000.577NaNNaNNaN0.5431.0000.429-0.288-0.1790.8810.5770.0001.0000.9520.0000.333-0.257-0.2571.000
BIA-BIA_DEE0.6880.9520.7380.9051.0000.8100.9050.6670.8810.8810.8160.8570.9290.9050.9520.8810.3160.8490.1670.385-0.2000.0000.7900.0001.0001.0001.0001.0000.7120.8160.0120.0000.1710.0000.6590.0000.316NaNNaNNaN0.4290.4510.429-0.5230.0360.8100.3160.0711.0000.8570.1670.304-0.314-0.3140.000
BIA-BIA_ECW0.3940.9050.6430.9520.8101.0000.9520.6190.8570.8570.1490.7860.8810.9520.9050.9760.0000.8370.4030.577-0.2000.0000.3710.5771.0001.0001.0001.0000.3310.577-0.1200.2040.0240.2040.5150.1490.224NaNNaNNaN0.3711.0000.381-0.252-0.1070.8810.224-0.1071.0000.9290.4030.365-0.257-0.2570.000
BIA-BIA_FFM0.5770.9290.7141.0000.9050.9521.0000.6900.9050.9050.4080.8330.9521.0000.9290.9760.0000.9460.0000.408-0.2001.0000.5630.4081.0001.0001.0001.0000.5650.4080.0720.0000.2200.0000.6110.0000.577NaNNaNNaN0.5431.0000.429-0.288-0.1790.8810.5770.0001.0000.9520.0000.333-0.257-0.2571.000
BIA-BIA_FFMI0.0000.6670.9760.6900.6670.6190.6901.0000.8810.8810.0000.8100.5480.6900.6670.7380.3290.6060.7750.000-0.4000.0000.7310.5771.0001.0001.0001.0000.5280.5770.2410.5770.3660.5770.9220.1490.224NaNNaNNaN0.8861.0000.857-0.360-0.1430.3810.2240.0001.0000.8330.7750.000-0.143-0.1430.000
BIA-BIA_FMI0.0000.8810.9290.9050.8810.8570.9050.8811.0001.0000.2920.9520.8330.9050.8810.9290.2360.8370.6770.000-0.2000.0000.7430.3331.0001.0001.0001.0000.6140.0000.1930.7070.3420.7070.8380.2920.000NaNNaNNaN0.8860.0000.595-0.342-0.1430.7380.0000.1791.0000.9760.6770.000-0.257-0.2570.000
BIA-BIA_Fat0.3320.8810.9290.9050.8810.8570.9050.8811.0001.0000.4350.9520.8330.9050.8810.9290.0000.8370.0000.333-0.2001.0000.7430.4561.0001.0001.0001.0000.6140.7070.1930.0000.3420.0000.8380.0000.274NaNNaNNaN0.8860.7070.595-0.342-0.1430.7380.2740.1791.0000.9760.0000.000-0.257-0.2570.000
BIA-BIA_Frame_num0.2940.7070.0000.4080.8160.1490.4080.0000.2920.4351.0000.4080.1490.4080.5770.4080.0610.5960.0000.0000.0000.0000.0000.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000NaNNaNNaN0.0000.0000.0000.0000.0000.5770.0000.0001.0000.5770.0000.0000.0000.1770.000
BIA-BIA_ICW0.0000.8810.8810.8330.8570.7860.8330.8100.9520.9520.4081.0000.7860.8330.8810.8810.0000.8120.2500.408-0.4001.0000.7310.4081.0001.0001.0001.0000.6380.4080.2050.0000.3170.0000.8500.4080.000NaNNaNNaN0.7710.7070.452-0.252-0.1070.7140.0000.3211.0000.9290.2500.333-0.371-0.3710.000
BIA-BIA_LDM0.0000.9050.6190.9520.9290.8810.9520.5480.8330.8330.1490.7861.0000.9520.9050.9050.0000.9330.4030.000-0.2001.0000.5870.0001.0000.0001.0000.0000.6870.0000.1080.2040.2680.2040.4670.0000.371NaNNaNNaN0.4290.5650.238-0.378-0.2500.9520.3710.0361.0000.8570.4030.000-0.086-0.0860.000
BIA-BIA_LST0.5770.9290.7141.0000.9050.9521.0000.6900.9050.9050.4080.8330.9521.0000.9290.9760.0000.9460.0000.408-0.2001.0000.5630.4081.0001.0001.0001.0000.5650.4080.0720.0000.2200.0000.6110.0000.577NaNNaNNaN0.5431.0000.429-0.288-0.1790.8810.5770.0001.0000.9520.0000.333-0.257-0.2571.000
BIA-BIA_SMM0.0001.0000.7140.9290.9520.9050.9290.6670.8810.8810.5770.8810.9050.9291.0000.9520.0000.8370.0000.000-0.2001.0000.6230.2041.0000.0001.0000.0000.5400.000-0.1450.2040.0000.2040.6470.1490.000NaNNaNNaN0.3140.3330.429-0.396-0.0360.8330.0000.0361.0000.9050.0000.000-0.314-0.3140.000
BIA-BIA_TBW0.0000.9520.7620.9760.8810.9760.9760.7380.9290.9290.4080.8810.9050.9760.9521.0000.0000.8850.2500.408-0.2001.0000.5270.4081.0001.0001.0001.0000.4790.408-0.0240.0000.1220.0000.6590.4080.000NaNNaNNaN0.5430.7070.476-0.288-0.1790.8570.0000.0001.0000.9760.2500.333-0.257-0.2570.000
BIA-Season0.0000.0000.3290.0000.3160.0000.0000.3290.2360.0000.0610.0000.0000.0000.0000.0001.0000.0000.3980.0000.0000.0000.5770.0001.0001.0001.0001.0000.5110.0000.0000.0000.0000.0000.0000.0000.806NaNNaNNaN0.0000.1670.5770.2500.0000.0000.8060.0001.0000.4080.3980.3160.0000.0000.667
Basic_Demos-Age0.0000.8370.6430.9460.8490.8370.9460.6060.8370.8370.5960.8120.9330.9460.8370.8850.0001.0000.1110.000-0.2360.0000.6370.0001.0000.0001.0000.0000.7220.000-0.1050.0000.0730.0000.4140.0000.1821.0001.0001.0000.1280.000-0.006-0.190-0.1820.8850.2720.3260.0000.5820.2140.1360.0550.0550.000
Basic_Demos-Enroll_Season0.0000.0000.7750.0000.1670.4030.0000.7750.6770.0000.0000.2500.4030.0000.0000.2500.3980.1111.0000.2870.0000.0000.4890.4941.0000.8161.0000.3330.0000.0000.0000.0000.0000.3950.0000.4900.3251.0001.0001.0000.0000.5370.3550.2770.1500.2110.2660.4880.0000.3181.0000.0000.0000.0000.289
Basic_Demos-Sex0.0000.3330.0000.4080.3850.5770.4080.0000.0000.3330.0000.4080.0000.4080.0000.4080.0000.0000.2871.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.7980.0000.0000.0000.3570.1660.1021.0001.0001.0000.2180.2180.0000.0000.0000.0000.0000.0000.5770.0000.1580.0000.0000.3270.000
CGAS-CGAS_Score0.707-0.200-0.200-0.200-0.200-0.200-0.200-0.400-0.200-0.2000.000-0.400-0.200-0.200-0.200-0.2000.000-0.2360.0000.0001.0000.6530.0560.3651.0001.0001.0001.0000.0000.0000.0280.000-0.2480.000-0.4530.0000.0001.0001.0001.000-0.3160.000-0.3820.0450.638-0.3270.0000.588NaN-0.5460.0000.000-0.378-0.3780.000
CGAS-Season0.7070.0000.0001.0000.0000.0001.0000.0000.0001.0000.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.6531.0000.0000.5001.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1671.0001.0001.0001.0000.0000.0000.0000.0000.7750.5770.612NaN0.0000.0000.0000.0000.0000.236
FGC-FGC_CU0.0000.6230.8020.5630.7900.3710.5630.7310.7430.7430.0000.7310.5870.5630.6230.5270.5770.6370.4890.0000.0560.0001.0000.4791.0000.5771.0000.5770.8230.6740.1310.0000.2590.0000.4690.0000.0001.0001.0001.0000.2860.7070.219-0.3070.2390.5400.0000.2880.0000.4400.4890.000-0.360-0.3600.000
FGC-FGC_CU_Zone0.0000.0000.5770.4080.0000.5770.4080.5770.3330.4560.0000.4080.0000.4080.2040.4080.0000.0000.4940.0000.3650.5000.4791.0001.0000.0001.0000.3470.0000.0000.0000.0000.0660.0000.0000.0000.0001.0001.0001.0000.2040.5270.0000.2360.0000.5660.0000.3850.0000.2460.4940.0000.0000.0000.000
FGC-FGC_GSD1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaNNaNNaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
FGC-FGC_GSD_Zone1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0001.0001.0000.0000.8160.0001.0001.0000.5770.0001.0001.0001.0000.0000.5770.0000.5770.0000.0000.0000.0000.0000.000NaNNaNNaN0.0000.8160.0000.0001.0000.0000.0000.3331.0001.0000.8160.0001.0001.0000.000
FGC-FGC_GSND1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaNNaNNaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
FGC-FGC_GSND_Zone1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.0001.0001.0000.0000.3330.0001.0001.0000.5770.3471.0000.0001.0001.0000.0000.3470.0000.0000.5770.0000.0000.0000.000NaNNaNNaN0.5770.3330.0000.0001.0000.3470.0000.3331.0001.0000.3330.0001.0001.0000.000
FGC-FGC_PU0.5530.5400.6140.5650.7120.3310.5650.5280.6140.6140.0000.6380.6870.5650.5400.4790.5110.7220.0000.0000.0000.0000.8230.0001.0000.5771.0000.0001.0000.7040.1990.0000.3160.0000.2510.1450.4031.0001.0001.000-0.0120.000-0.255-0.345-0.1530.6570.4030.4170.0000.1360.0000.4750.0430.0430.413
FGC-FGC_PU_Zone0.9130.7070.5770.4080.8160.5770.4080.5770.0000.7070.0000.4080.0000.4080.0000.4080.0000.0000.0000.0000.0000.0000.6740.0001.0000.0001.0000.3470.7041.0000.4550.0000.1570.0000.0000.0000.0001.0001.0001.0000.5770.3850.3400.3180.6320.3400.0000.2151.0000.6740.0000.1730.0000.0000.474
FGC-FGC_SRL0.394-0.1450.2770.0720.012-0.1200.0720.2410.1930.1930.0000.2050.1080.072-0.145-0.0240.000-0.1050.0000.7980.0280.0000.1310.0001.0000.5771.0000.0000.1990.4551.0000.7980.9690.6320.1420.0000.1921.0001.0001.0000.5330.0000.0000.561-0.433-0.1860.1920.2060.0000.0420.0000.000-0.017-0.0170.000
FGC-FGC_SRL_Zone0.3650.3330.5770.0000.0000.2040.0000.5770.7070.0000.0000.0000.2040.0000.2040.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.7981.0000.6740.6610.0000.0000.0001.0001.0001.0000.1490.0000.0000.0000.1510.0000.0000.0000.5770.0660.0000.0000.0000.0000.000
FGC-FGC_SRR0.6320.0000.4150.2200.1710.0240.2200.3660.3420.3420.0000.3170.2680.2200.0000.1220.0000.0730.0000.000-0.2480.0000.2590.0661.0000.0001.0000.5770.3160.1570.9690.6741.0000.6740.2150.0000.2481.0001.0001.0000.6100.0000.0610.460-0.502-0.0100.2480.0990.0000.1800.0000.0000.1110.1110.172
FGC-FGC_SRR_Zone0.3650.3330.5770.0000.0000.2040.0000.5770.7070.0000.0000.0000.2040.0000.2040.0000.0000.0000.3950.0000.0000.0000.0000.0001.0000.0001.0000.0000.0000.0000.6320.6610.6741.0000.3570.0000.0001.0001.0001.0000.0000.0000.0000.0000.4400.0000.0000.0000.0000.0000.3950.0000.0000.0000.000
FGC-FGC_TL0.0000.6470.9220.6110.6590.5150.6110.9220.8380.8380.0000.8500.4670.6110.6470.6590.0000.4140.0000.357-0.4530.0000.4690.0001.0000.0001.0000.0000.2510.0000.1420.0000.2150.3571.0000.6030.0001.0001.0001.0000.2860.1180.537-0.152-0.2540.2800.000-0.0831.0000.6780.0000.076-0.576-0.5760.000
FGC-FGC_TL_Zone0.0000.0000.1490.0000.0000.1490.0000.1490.2920.0000.0000.4080.0000.0000.1490.4080.0000.0000.4900.1660.0000.0000.0000.0001.0000.0001.0000.0000.1450.0000.0000.0000.0000.0000.6031.0000.1741.0001.0001.0000.0000.0000.2930.5500.0000.1420.1740.1000.5770.0000.4900.3350.0000.0000.218
FGC-Season0.0000.0000.2240.5770.3160.2240.5770.2240.0000.2740.0000.0000.3710.5770.0000.0000.8060.1820.3250.1020.0000.1670.0000.0001.0000.0001.0000.0000.4030.0000.1920.0000.2480.0000.0000.1741.0001.0001.0001.0000.0000.0000.4300.1030.1620.0001.0000.2230.0000.4710.3250.2640.0000.0000.745
Fitness_Endurance-Max_StageNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0001.0001.0001.0001.0001.000NaNNaNNaNNaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
Fitness_Endurance-Time_MinsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0001.0001.0001.0001.0001.000NaNNaNNaNNaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
Fitness_Endurance-Time_SecNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.0001.0001.0001.0001.0001.0001.000NaNNaNNaNNaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
PAQ_C-PAQ_C_Total0.0000.3140.8860.5430.4290.3710.5430.8860.8860.8860.0000.7710.4290.5430.3140.5430.0000.1280.0000.218-0.3161.0000.2860.2041.0000.0001.0000.577-0.0120.5770.5330.1490.6100.0000.2860.0000.0001.0001.0001.0001.0000.0860.8100.1560.000-0.2620.0000.2871.0000.6900.0000.581-0.464-0.4640.000
PAQ_C-Season0.0000.0001.0001.0000.4511.0001.0001.0000.0000.7070.0000.7070.5651.0000.3330.7070.1670.0000.5370.2180.0000.0000.7070.5271.0000.8161.0000.3330.0000.3850.0000.0000.0000.0000.1180.0000.0001.0001.0001.0000.0861.0000.0000.5860.1180.0000.0000.3861.0000.0000.5370.0001.0000.3160.500
Physical-BMI0.0000.4290.7620.4290.4290.3810.4290.8570.5950.5950.0000.4520.2380.4290.4290.4760.577-0.0060.3550.000-0.3820.0000.2190.0001.0000.0001.0000.000-0.2550.3400.0000.0000.0610.0000.5370.2930.4301.0001.0001.0000.8100.0001.0000.0230.168-0.2060.430-0.4330.0000.6100.3550.278-0.117-0.1170.000
Physical-Diastolic_BP0.000-0.396-0.414-0.288-0.523-0.252-0.288-0.360-0.342-0.3420.000-0.252-0.378-0.288-0.396-0.2880.250-0.1900.2770.0000.0450.000-0.3070.2361.0000.0001.0000.000-0.3450.3180.5610.0000.4600.000-0.1520.5500.1031.0001.0001.0000.1560.5860.0231.0000.000-0.3700.1030.4000.7070.1510.2770.000-0.218-0.2180.000
Physical-HeartRate0.000-0.036-0.107-0.1790.036-0.107-0.179-0.143-0.143-0.1430.000-0.107-0.250-0.179-0.036-0.1790.000-0.1820.1500.0000.6380.0000.2390.0001.0001.0001.0001.000-0.1530.632-0.4330.151-0.5020.440-0.2540.0000.1621.0001.0001.0000.0000.1180.1680.0001.000-0.0880.1620.1320.000-0.0630.1500.000-0.533-0.5330.000
Physical-Height0.0000.8330.4760.8810.8100.8810.8810.3810.7380.7380.5770.7140.9520.8810.8330.8570.0000.8850.2110.000-0.3270.7750.5400.5661.0000.0001.0000.3470.6570.340-0.1860.000-0.0100.0000.2800.1420.0001.0001.0001.000-0.2620.000-0.206-0.370-0.0881.0000.0000.0960.0000.5140.2110.000-0.167-0.1670.192
Physical-Season0.0000.0000.2240.5770.3160.2240.5770.2240.0000.2740.0000.0000.3710.5770.0000.0000.8060.2720.2660.0000.0000.5770.0000.0001.0000.0001.0000.0000.4030.0000.1920.0000.2480.0000.0000.1741.0001.0001.0001.0000.0000.0000.4300.1030.1620.0001.0000.2230.0000.4710.2660.2080.0000.0000.694
Physical-Systolic_BP0.3540.0360.1070.0000.071-0.1070.0000.0000.1790.1790.0000.3210.0360.0000.0360.0000.0000.3260.4880.0000.5880.6120.2880.3851.0000.3331.0000.3330.4170.2150.2060.0000.0990.000-0.0830.1000.2231.0001.0001.0000.2870.386-0.4330.4000.1320.0960.2231.0000.000-0.0640.4880.408-0.435-0.4350.192
Physical-Waist_Circumference1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.577NaNNaN0.0000.0001.0001.0001.0001.0000.0001.0000.0000.5770.0000.0001.0000.5770.0000.0000.0000.0001.0001.0000.0000.7070.0000.0000.0000.0001.0000.0000.0001.0001.0001.0001.000
Physical-Weight0.3940.9050.8570.9520.8570.9290.9520.8330.9760.9760.5770.9290.8570.9520.9050.9760.4080.5820.3180.000-0.5460.0000.4400.2461.0001.0001.0001.0000.1360.6740.0420.0660.1800.0000.6780.0000.4711.0001.0001.0000.6900.0000.6100.151-0.0630.5140.471-0.0640.0001.0000.3180.000-0.400-0.4000.000
PreInt_EduHx-Season0.0000.0000.7750.0000.1670.4030.0000.7750.6770.0000.0000.2500.4030.0000.0000.2500.3980.2141.0000.1580.0000.0000.4890.4941.0000.8161.0000.3330.0000.0000.0000.0000.0000.3950.0000.4900.3251.0001.0001.0000.0000.5370.3550.2770.1500.2110.2660.4880.0000.3181.0000.0000.0000.0000.289
PreInt_EduHx-computerinternet_hoursday0.6880.3190.0000.3330.3040.3650.3330.0000.0000.0000.0000.3330.0000.3330.0000.3330.3160.1360.0000.0000.0000.0000.0000.0001.0000.0001.0000.0000.4750.1730.0000.0000.0000.0000.0760.3350.2641.0001.0001.0000.5810.0000.2780.0000.0000.0000.2080.4081.0000.0000.0001.0000.0000.0000.158
SDS-SDS_Total_Raw0.707-0.314-0.257-0.257-0.314-0.257-0.257-0.143-0.257-0.2570.000-0.371-0.086-0.257-0.314-0.2570.0000.0550.0000.000-0.3780.000-0.3600.0001.0001.0001.0001.0000.0430.000-0.0170.0000.1110.000-0.5760.0000.0001.0001.0001.000-0.4641.000-0.117-0.218-0.533-0.1670.000-0.4351.000-0.4000.0000.0001.0001.0000.000
SDS-SDS_Total_T0.000-0.314-0.257-0.257-0.314-0.257-0.257-0.143-0.257-0.2570.177-0.371-0.086-0.257-0.314-0.2570.0000.0550.0000.327-0.3780.000-0.3600.0001.0001.0001.0001.0000.0430.000-0.0170.0000.1110.000-0.5760.0000.0001.0001.0001.000-0.4640.316-0.117-0.218-0.533-0.1670.000-0.4351.000-0.4000.0000.0001.0001.0000.000
SDS-Season0.0000.0000.0001.0000.0000.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.6670.0000.2890.0000.0000.2360.0000.0001.0000.0001.0000.0000.4130.4740.0000.0000.1720.0000.0000.2180.7451.0001.0001.0000.0000.5000.0000.0000.0000.1920.6940.1921.0000.0000.2890.1580.0000.0001.000

Missing values

2024-10-11T10:26:06.238045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-11T10:26:06.970013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-10-11T10:26:08.534088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idBasic_Demos-Enroll_SeasonBasic_Demos-AgeBasic_Demos-SexCGAS-SeasonCGAS-CGAS_ScorePhysical-SeasonPhysical-BMIPhysical-HeightPhysical-WeightPhysical-Waist_CircumferencePhysical-Diastolic_BPPhysical-HeartRatePhysical-Systolic_BPFitness_Endurance-SeasonFitness_Endurance-Max_StageFitness_Endurance-Time_MinsFitness_Endurance-Time_SecFGC-SeasonFGC-FGC_CUFGC-FGC_CU_ZoneFGC-FGC_GSNDFGC-FGC_GSND_ZoneFGC-FGC_GSDFGC-FGC_GSD_ZoneFGC-FGC_PUFGC-FGC_PU_ZoneFGC-FGC_SRLFGC-FGC_SRL_ZoneFGC-FGC_SRRFGC-FGC_SRR_ZoneFGC-FGC_TLFGC-FGC_TL_ZoneBIA-SeasonBIA-BIA_Activity_Level_numBIA-BIA_BMCBIA-BIA_BMIBIA-BIA_BMRBIA-BIA_DEEBIA-BIA_ECWBIA-BIA_FFMBIA-BIA_FFMIBIA-BIA_FMIBIA-BIA_FatBIA-BIA_Frame_numBIA-BIA_ICWBIA-BIA_LDMBIA-BIA_LSTBIA-BIA_SMMBIA-BIA_TBWPAQ_A-SeasonPAQ_A-PAQ_A_TotalPAQ_C-SeasonPAQ_C-PAQ_C_TotalSDS-SeasonSDS-SDS_Total_RawSDS-SDS_Total_TPreInt_EduHx-SeasonPreInt_EduHx-computerinternet_hoursday
000008ff9Fall50Winter51.0Fall16.87731646.0050.8NaNNaNNaNNaNNaNNaNNaNNaNFall0.00.0NaNNaNNaNNaN0.00.07.00.06.00.06.01.0Fall2.02.6685516.8792932.4981492.008.2559841.586213.81773.061439.213771.024.43498.8953638.917719.541332.6909NaNNaNNaNNaNNaNNaNNaNFall3.0
1000fd460Summer90NaNNaNFall14.03559048.0046.022.075.070.0122.0NaNNaNNaNNaNFall3.00.0NaNNaNNaNNaN5.00.011.01.011.01.03.00.0Winter2.02.5794914.0371936.6561498.656.0199342.029112.82541.211723.970851.021.035214.9740039.449715.410727.0552NaNNaNFall2.340Fall46.064.0Summer0.0
200105258Summer101Fall71.0Fall16.64869656.5075.6NaN65.094.0117.0Fall5.07.033.0Fall20.01.010.21.014.72.07.01.010.01.010.01.05.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSummer2.170Fall38.054.0Summer2.0
300115b9fWinter90Fall71.0Summer18.29234756.0081.6NaN60.097.0117.0Summer6.09.037.0Summer18.01.0NaNNaNNaNNaN5.00.07.00.07.00.07.01.0Summer3.03.8419118.29431131.4301923.4415.5925062.775714.07404.2203318.824302.030.404116.7790058.933826.479845.9966NaNNaNWinter2.451Summer31.045.0Winter0.0
40016bb22Spring181SummerNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSummer1.04NaNNaNNaNNaNNaNNaNNaN
5001f3379Spring131Winter50.0Summer22.27995259.50112.2NaN60.073.0102.0NaNNaNNaNNaNSummer12.00.016.52.017.92.06.00.010.01.011.01.08.00.0Summer2.04.3303630.18651330.9701996.4530.2124084.028516.687713.4988067.971502.032.914120.9020079.698235.380463.1265NaNNaNSpring4.110Summer40.056.0Spring0.0
60038ba98Fall100NaNNaNFall19.66076055.0084.6NaN123.083.0163.0NaNNaNNaNNaNFall9.01.0NaNNaNNaNNaN2.00.011.01.011.01.011.01.0Fall2.03.7827119.66291135.8601817.3816.3275063.247014.70004.9629121.353002.030.893616.0259059.464326.195747.2211NaNNaNWinter3.670Winter27.040.0Fall3.0
70068a485Fall101NaNNaNFall16.86128659.2584.227.071.090.0116.0NaNNaNNaNNaNFall0.00.012.62.011.11.00.00.00.00.00.00.04.00.0Fall3.04.0572616.86311180.0401888.0621.9400067.952713.60923.2539516.247402.028.536717.4760063.895428.768050.4767NaNNaNFall1.270NaNNaNNaNFall2.0
80069fbedSummer150NaNNaNSpringNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSpringNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSummer2.0
90083e397Summer191SummerNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
idBasic_Demos-Enroll_SeasonBasic_Demos-AgeBasic_Demos-SexCGAS-SeasonCGAS-CGAS_ScorePhysical-SeasonPhysical-BMIPhysical-HeightPhysical-WeightPhysical-Waist_CircumferencePhysical-Diastolic_BPPhysical-HeartRatePhysical-Systolic_BPFitness_Endurance-SeasonFitness_Endurance-Max_StageFitness_Endurance-Time_MinsFitness_Endurance-Time_SecFGC-SeasonFGC-FGC_CUFGC-FGC_CU_ZoneFGC-FGC_GSNDFGC-FGC_GSND_ZoneFGC-FGC_GSDFGC-FGC_GSD_ZoneFGC-FGC_PUFGC-FGC_PU_ZoneFGC-FGC_SRLFGC-FGC_SRL_ZoneFGC-FGC_SRRFGC-FGC_SRR_ZoneFGC-FGC_TLFGC-FGC_TL_ZoneBIA-SeasonBIA-BIA_Activity_Level_numBIA-BIA_BMCBIA-BIA_BMIBIA-BIA_BMRBIA-BIA_DEEBIA-BIA_ECWBIA-BIA_FFMBIA-BIA_FFMIBIA-BIA_FMIBIA-BIA_FatBIA-BIA_Frame_numBIA-BIA_ICWBIA-BIA_LDMBIA-BIA_LSTBIA-BIA_SMMBIA-BIA_TBWPAQ_A-SeasonPAQ_A-PAQ_A_TotalPAQ_C-SeasonPAQ_C-PAQ_C_TotalSDS-SeasonSDS-SDS_Total_RawSDS-SDS_Total_TPreInt_EduHx-SeasonPreInt_EduHx-computerinternet_hoursday
100087dd65Spring111NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSpringNaN
1100abe655Fall110Summer66.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNWinterNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNWinter1.10Winter42.059.0Fall0.0
1200ae59c9Fall130NaNNaNWinter21.07906557.75100.0NaN63.079.0150.0NaNNaNNaNNaNWinter24.01.019.21.018.41.020.01.08.01.09.51.012.51.0Winter5.05.0802521.08141239.462974.7119.954574.282315.65975.4216525.71772.036.057218.270569.202036.223256.0118NaNNaNFall3.02Fall33.047.0Fall1.0
1300af6387Spring120NaNNaNSpring15.54411160.0079.624.057.071.0103.0NaNNaNNaNNaNSpring10.00.022.32.021.62.07.00.07.00.09.01.012.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSpring1.22NaNNaNNaNSpringNaN
1400bd4359Spring120NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSpringNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSpring2.0
1500c0cd71Winter70Summer51.0Spring29.31577554.00121.6NaN80.075.099.0Spring4.05.032.0Spring6.01.0NaNNaNNaNNaN0.00.012.01.015.01.012.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSpring35.050.0Winter2.0
1600d56d4bSpring51Summer80.0Spring17.28450444.0047.6NaN61.076.0109.0SpringNaNNaNNaNSpring0.00.0NaNNaNNaNNaN0.00.010.51.010.01.07.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNSpring37.053.0Spring0.0
1700d9913dFall101NaNNaNFall19.89315755.0085.630.0NaN81.0NaNNaNNaNNaNNaNFall5.00.0NaNNaNNaNNaN0.00.00.00.00.00.09.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNFall1.0
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